Index

A

• A* algorithm
  • URL / Finding the shortest path
• Acbracad library
  • URL / Distributed unique IDs with Hadoop
• AcmeContent
  • about / Introducing AcmeContent
  • sample code / Download the sample code
• acyclic / Visualizing graphs with Loom
• Adaptive Boosting (AdaBoost) / Bagging and boosting
• Akaike Information Criterion (AIC)
  • models, identifying / Identifying better models with Akaike Information Criterion
  • about / Identifying better models with Akaike Information Criterion
• ALS
  • movie recommendations / Movie recommendations with alternating least squares
  • using, with Spark / ALS with Spark and MLlib
  • using, with MLlib / ALS with Spark and MLlib
  • used, for making predictions / Making predictions with ALS
  • evaluating / Evaluating ALS
• Anscombe's Quartet / The importance of visualizations
• Apache Commons Math
  • about / Estimating the maximum likelihood, Nelder-Mead optimization with Apache Commons Math
  • URL / Estimating the maximum likelihood, Nelder-Mead optimization with Apache Commons Math
  • used, for Nelder-Mead optimization / Nelder-Mead optimization with Apache Commons Math
• ARMA model order
  • determining, with ACF and PACF / Determining ARMA model order with ACF and PACF
• autocorrelation function (ACF)
  • about / Determining autocorrelation in AR models
  • ARMA model order, determining / Determining ARMA model order with ACF and PACF
  • plotting, of airline data / ACF and PACF of airline data
• autocovariance
  • about / Autocovariance
• autoregressive (AR) models
  • about / Autoregressive models
  • autocorrelation, determining / Determining autocorrelation in AR models
  • combining, with Moving Average (MA) models / Combining the AR and MA models
• Autoregressive Integrated Moving Average (ARIMA) model
  • about / Removing seasonality with differencing

B

• B1
  • about / B1
  • URL / B1
• bag-of-words / The bag-of-words and Euclidean distance
• bagging
  • about / Bagging and boosting
• balanced F-score / F-measure and the harmonic mean
• batch gradient descent
  • about / Stochastic gradient descent
• Bayesian view / Probability
• Bayes theorem
  • about / Bayes theorem
  • with multiple predictors / Bayes theorem with multiple predictors, Naive Bayes classification
• bias
  • about / Bias
  • high bias, addressing / Addressing high bias
• bias term / Multiple linear regression
• big data
  • code, downloading / Downloading the code and data
  • example code, URL / Downloading the code and data
  • inspecting / Inspecting the data
  • records, counting / Counting the records
• bigrams
  • about / Better clustering with n-grams
• bimodal
  • about / Visualizing different populations
• binning
  • about / Binning data
• binomial distribution
  • about / The binomial distribution
• bipartite / Visualizing graphs with Loom
• bivariate linear regression / Multiple linear regression
• Bloom filters
  • used, for testing large sets membership / Testing set membership with Bloom filters
• Bonferroni correction
  • about / The Bonferroni correction
• boosting
  • about / Bagging and boosting
• bounce
  • about / Introducing AcmeContent
• box and whisker plots
  • about / Box plots
• breadth-first search / Breadth-first and depth-first search

C

• C4.5 algorithm / Building a decision tree in clj-ml
• categorical variables / Categorical and dummy variables
• central limit theorem / The central limit theorem
  • about / The central limit theorem
• Chi-squared multiple significance testing
  • about / Chi-squared multiple significance testing
  • categories, visualizing / Visualizing the categories
  • chi-squared test / The chi-squared test, The chi-squared test
  • chi-squared statistic / The chi-squared statistic
• chi-squared statistic / The chi-squared statistic
• chi-squared test / The chi-squared test, The chi-squared test
• classifier
  • data / About the data
  • data, inspecting / Inspecting the data
  • relative risk and odds, comparing with / Comparisons with relative risk and odds
  • saving, to file / Saving the classifier to a file
• clj-ml
  • classification with / Classification with clj-ml
  • URL / Classification with clj-ml
  • data, loading with / Loading data with clj-ml
  • decision tree, building / Building a decision tree in clj-ml
• clj-time library
  • URL / Inspecting the airline data
• clojure-opennlp library
  • URL / Tokenizing the Reuters files
• Clojure libraries
  • URL / Exploratory data visualization
• Clojure library succession
  • URL / Calculating the likelihood
• Clojure library Tesser
  • URL / Mathematical folds with Tesser
• Clojure reducers library
  • URL / Counting the records
  • about / The reducers library
  • parallel folds / Parallel folds with reducers
  • parallel folds with / Parallel folds with reducers
  • large files, loading with iota / Loading large files with iota
  • reducers processing pipeline, creating / Creating a reducers processing pipeline
  • curried reductions, with reducers / Curried reductions with reducers
  • statistical folds / Statistical folds with reducers
  • associativity / Associativity
  • mean calculating, fold used / Calculating the mean using fold
  • variance calculating, fold used / Calculating the variance using fold
• cluster evaluation, measures
  • about / Cluster evaluation measures
  • inter-cluster density / Inter-cluster density
  • intra-cluster density / Intra-cluster density
  • root mean square error, calculating with Parkour / Calculating the root mean square error with Parkour
  • clustered points and centroids, loading / Loading clustered points and centroids
  • cluster RMSE, calculating / Calculating the cluster RMSE
  • optimal k, determining with elbow method / Determining optimal k with the elbow method
  • optimal k, determining with Dunn index / Determining optimal k with the Dunn index
  • optimal k, determining with Davies-Bouldin index / Determining optimal k with the Davies-Bouldin index
• clustering
  • data, downloading / Downloading the data
  • data, extracting / Extracting the data
  • data, inspecting / Inspecting the data
• clustering, text
  • about / Clustering text
  • set-of-words / Set-of-words and the Jaccard index
  • Jaccard index / Set-of-words and the Jaccard index
  • Reuters files, tokenizing / Tokenizing the Reuters files
  • text, representing as vectors / Representing text as vectors
  • dictionary, creating / Creating a dictionary
• cluster RMSE
  • calculating / Calculating the cluster RMSE
• code
  • downloading / Download the code and data
  • downloading, URL / Download the code and data
• coefficient of determination / Goodness-of-fit and R-square
• coefficient of multiple determination / Multiple R-squared
• collinearity
  • about / Collinearity
  • multicollinearity / Multicollinearity
• columns
  • adding / Adding columns, Adding derived columns
• combinations function
  • URL / Determining optimal k with the Dunn index
• communities, with label propagation
  • detecting / Detecting communities with label propagation
  • map vertices / Step one – map vertices
  • vertex attribute, sending / Step two – send the vertex attribute
  • aggregate value / Step three – aggregate value
  • vertex function / Step four – vertex function
  • maximum iterations count, setting / Step five – set the maximum iterations count
• comparative visualizations
  • about / Comparative visualizations
  • box and whisker plots / Box plots
  • cumulative distribution functions / Cumulative distribution functions
  • probability mass function (PMF) / Probability mass functions
  • scatter plots / Scatter plots
  • scatter transparency / Scatter transparency
• confidence interval
  • about / Confidence intervals, Confidence intervals
• confounding variables
  • about / Regression
• confusion matrix / The confusion matrix
• connected components
  • running / Running connected components
  • largest connected component, size calculating / Calculating the size of the largest connected component
• connected components, with Pregel API
  • about / Connected components with the Pregel API
  • map vertices / Step one – map vertices
  • message function / Steps two and three – the message function
  • attributes, updating / Step four – update the attributes
  • convergence, iterating to / Step five – iterate to convergence
• construction
  • about / Construction
• content-based filtering / Types of recommender systems
• content distribution network (CDN) / jStat
• covariance
  • about / Covariance
  • calculating, with Tesser / Calculating covariance with Tesser
• cross-validation
  • about / Cross-validation
• cumulative distribution function (CDF)
  • about / Hypothesis testing
• Cumulative distribution functions (CDFs)
  • about / Cumulative distribution functions

D

• daily means distribution
  • about / The distribution of daily means
• data
  • inspecting / Load and inspect the data, Inspecting the data, Inspect the data
  • loading / Load and inspect the data
  • about / About the data
  • Guardian's excellent data blog, URL / About the data
  • visualizing / Visualizing the data
  • downloading / Download the code and data
  • downloading, URL / Download the code and data
  • parsing / Parse the data
• data scrubbing
  • about / Data scrubbing
• Davies-Bouldin index
  • used, for determining optimal k / Determining optimal k with the Davies-Bouldin index
• decision trees
  • about / Decision trees
  • information / Information
  • entropy / Entropy
  • information gain / Information gain
  • information gain, using to identify best predictor / Using information gain to identify the best predictor
  • building, recursively / Recursively building a decision tree
  • using, for classification / Using the decision tree for classification
  • classifier, evaluating / Evaluating the decision tree classifier
  • building, in clj-ml / Building a decision tree in clj-ml
• degenerate matrices / Inversion
• degrees of freedom
  • about / Student's t-distribution, Degrees of freedom
• Delta rule / The gradient descent update rule
• dependent variable
  • about / Regression
• depth-first search / Breadth-first and depth-first search
• descriptive statistics
  • about / Descriptive statistics
  • mean / The mean
  • mathematical notation, interpreting / Interpreting mathematical notation
  • median / The median
• dictionary
  • creating / Creating a dictionary
• dimensionality reduction
  • about / Dimensionality reduction
  • Iris dataset, plotting / Plotting the Iris dataset
  • principle component analysis (PCA) / Principle component analysis
  • principle component analysis(PCA) / Principle component analysis
  • Singular Value Decomposition (SVD) / Singular value decomposition
• dimensions
  • about / Dimensions
• Directed Acyclic Graph (DAG) / Visualizing graphs with Loom
• Discounted Cumulative Gain (DCG) / Normalized discounted cumulative gain
• discrete time models
  • about / Discrete time models
  • random walks / Random walks
  • autoregressive (AR) models / Autoregressive models
  • autocorrelation, determining in AR models / Determining autocorrelation in AR models
  • Moving Average (MA) models / Moving-average models
  • partial autocorrelation function (PACF), calculating / Calculating partial autocorrelation
  • seasonality, removing with differencing / Removing seasonality with differencing
• distance measures, evaluating
  • about / Evaluating distance measures
  • Pearson correlation similarity / The Pearson correlation similarity
  • Spearman’s rank similarity / Spearman's rank similarity
• distributed cache
  • data, sharing with / Sharing data with the distributed cache
• distributed unique IDs
  • creating / Creating distributed unique IDs
  • with Hadoop / Distributed unique IDs with Hadoop
• dot product / Matrix-vector multiplication
• dummy variables / Categorical and dummy variables
• Dunn index
  • used, for determining optimal k / Determining optimal k with the Dunn index
• Durbin-Levinson recursion
  • used, for calculating partial autocorrelation function (PACF) / PACF with Durbin-Levinson recursion
  • about / PACF with Durbin-Levinson recursion
  • URL / PACF with Durbin-Levinson recursion
• dwell time
  • about / Introducing AcmeContent
• dwell times
  • visualizing / Visualizing the dwell times

E

• edge-list format / Inspecting the data
• elbow method
  • used, for determining optimal k / Determining optimal k with the elbow method
• ensemble learning
  • about / Ensemble learning and random forests
• entropy / Entropy
• explained sum of squares (ESS) / The F-test of model significance
• exploratory data visualization / Exploratory data visualization
• exponential distribution
  • about / The exponential distribution

F

• F-distribution
  • about / The F-distribution
• F-statistic
  • about / The F-statistic
• F-test
  • about / The F-test
  / The F-test of model significance
• F1 measure / F-measure and the harmonic mean
• feature matrix
  • creating / Creating a feature matrix
• Fisher z-transformation
  • about / Confidence intervals
• Flambo
  • URL / Large-scale machine learning with Apache Spark and MLlib
• fold / Parallel folds with reducers
• frequency vectors / Representing text as vectors
• frequentist / Probability
• Fressian
  • URL / Chaining mappers and reducers with Parkour graph
• fs library
  • URL / Clustering the Reuters documents

G

• Gaussian distribution
  • about / The normal distribution
  • central limit theorem / The central limit theorem
• Giraph
  • URL / Distributed graph computation with GraphX
• GitHub
  • URL / Downloading the sample code
• Glittering
  • URL / Creating RDGs with Glittering
• gradient descent
  • about / The logistic regression cost function, Multiple regression with gradient descent
  • multiple regression with / Multiple regression with gradient descent
  • update rule / The gradient descent update rule
  • learning rate / The gradient descent learning rate
  • feature scaling / Feature scaling
  • feature extraction / Feature extraction
  • custom Tesser fold, creating / Creating a custom Tesser fold
  • total model error, calculating / Calculating the total model error
  • matrix-mean fold, creating / Creating a matrix-mean fold
  • single step, applying / Applying a single step of gradient descent
  • iterative gradient descent, running / Running iterative gradient descent
  • scaling with Hadoop / Scaling gradient descent with Hadoop
• gradient descent on Hadoop, with Tesser and Parkour
  • about / Gradient descent on Hadoop with Tesser and Parkour
  • Parkour distributed sources and sinks / Parkour distributed sources and sinks
  • feature scale fold, running with Hadoop / Running a feature scale fold with Hadoop
  • gradient descent, running with Hadoop / Running gradient descent with Hadoop
  • code, preparing for Hadoop cluster / Preparing our code for a Hadoop cluster
  • uberjar, building / Building an uberjar
  • uberjar, submitting to Hadoop / Submitting the uberjar to Hadoop
• graphs
  • visualizing, Loom used / Visualizing graphs with Loom
• graph traversal
  • with Loom / Graph traversal with Loom
  • Königsberg city, seven bridges / The seven bridges of Königsberg
• GraphViz
  • URL / Visualizing graphs with Loom
• GraphX / Scale-free networks
  • distributed graph computation / Distributed graph computation with GraphX
  • RDGs, creating with Glittering / Creating RDGs with Glittering
  • graph density, measuring with triangle counting / Measuring graph density with triangle counting
  • partitioning strategies / GraphX partitioning strategies
  • built-in triangle counting algorithm, running / Running the built-in triangle counting algorithm
  • triangle counting, implementing with Glittering / Implement triangle counting with Glittering
  • custom triangle counting algorithm, running / Running the custom triangle counting algorithm
  • Pregel API / The Pregel API
  • Pregel API, connected components / Connected components with the Pregel API
  • connected components, running / Running connected components
  • largest connected component, size calculating / Calculating the size of the largest connected component
  • communities with label propagation, detecting / Detecting communities with label propagation
  • label propagation, running / Running label propagation
  • flow formulation / The flow formulation
  • PageRank, implementing with Glittering / Implementing PageRank with Glittering
  • PageRank, running to determine community influencers / Running PageRank to determine community influencers
• gross domestic product (GDP)
  • about / About the data

H

• Hadoop Distributed File System (HDFS) / Large-scale machine learning with Apache Spark and MLlib
• Hadoop installation guides
  • URL / Submitting the uberjar to Hadoop
• Hama
  • URL / Distributed graph computation with GraphX
• heteroscedasticity
  • about / Visualizing the airline data
• histogram
  • about / Histograms
• hypothesis testing
  • about / Visualizing different populations, Hypothesis testing, Hypothesis testing
  • significance testing / Significance

I

• Ideal Discounted Cumulative Gain (IDCG) / Normalized discounted cumulative gain
• identity matrix / The identity matrix
• Incanter
  • gradient descent with / Gradient descent with Incanter
  • logistic regression, implementing with / Implementing logistic regression with Incanter
• Incanter's linear model
  • about / Incanter's linear model
  • F-test / The F-test of model significance
• Incanter library
  • URL / Inspecting the data
• independent variable
  • about / Regression
• indices function / Testing set membership with Bloom filters
• inferential statistics
  • about / Descriptive statistics
• information gain
  • about / Information gain
  • used, for identifying best predictor / Using information gain to identify the best predictor
• Information Retrieval statistics (IR stats) evaluator
  • about / Information retrieval statistics
  • precision / Precision
  • recall / Recall
  • of Mahout / Mahout's information retrieval evaluator
  • F-measure / F-measure and the harmonic mean
  • harmonic mean / F-measure and the harmonic mean
  • false positive rate / Fall-out
  • fall-out / Fall-out
  • Discounted Cumulative Gain (DCG) / Normalized discounted cumulative gain
  • normalized discounted cumulative gain / Normalized discounted cumulative gain
  • results, plotting / Plotting the information retrieval results
  • implicit, versus explicit feedback / Implicit versus explicit feedback
• inter-cluster density / Inter-cluster density
• interface
  • binding / Binding the interface
• interquartile range / Quantiles
• intra-cluster density / Intra-cluster density
• inversion matrix / Inversion
• iota
  • URL / Loading large files with iota
  • used, for loading large files / Loading large files with iota
• IRS data definition
  • URL / Inspecting the data
• IRS Statistics of Income (SoI)
  • URL / Downloading the code and data
• item-based recommenders
  • about / Item-based and user-based recommenders
  • practical considerations / Practical considerations for user and item recommenders

J

• Jaccard index / Set-of-words and the Jaccard index
  • applying, to documents / Applying the Jaccard index to documents
• jStat
  • about / jStat
  • URL / jStat

K

• k-means
  • drawbacks / The drawbacks of k-means
  • Mahalanobis distance measure / The Mahalanobis distance measure
  • dimensionality, curse / The curse of dimensionality
• k-means clustering
  • about / Clustering with k-means and Incanter
  • with Term Frequency-Inverse Document Frequency (TF-IDF) / k-means clustering with TF-IDF
• k-means clustering, running with Mahout
  • about / Running k-means clustering with Mahout
  • results, viewing / Viewing k-means clustering results
  • clustered output, interpreting / Interpreting the clustered output
• k-nearest neighbors (k-NN)
  • about / k-nearest neighbors
• kappa statistic model / The kappa statistic
• k hash functions / Testing set membership with Bloom filters

L

• label propagation
  • about / Detecting communities with label propagation
  • running / Running label propagation
• large-scale clustering, with Mahout
  • about / Large-scale clustering with Mahout
  • text documents, converting to sequence file / Converting text documents to a sequence file
  • Mahout vectors creating, Parkour used / Using Parkour to create Mahout vectors
  • distributed unique IDs, creating / Creating distributed unique IDs
  • distributed unique IDs, with Hadoop / Distributed unique IDs with Hadoop
  • data, sharing with distributed cache / Sharing data with the distributed cache
  • Mahout vectors, building from input documents / Building Mahout vectors from input documents
• large-scale machine learning
  • MLlib, using / Large-scale machine learning with Apache Spark and MLlib
  • Spark, using / Large-scale machine learning with Apache Spark and MLlib
  • data, loading with Sparkling / Loading data with Sparkling
  • data, mapping / Mapping data
  • tuples / Distributed datasets and tuples
  • distributed datasets / Distributed datasets and tuples
  • data, filtering / Filtering data
  • persistence / Persistence and caching
  • caching / Persistence and caching
• larger sets
  • probabilistic methods / Probabilistic methods for large sets
  • membership, testing with Bloom filters / Testing set membership with Bloom filters
  • Jaccard similarity, with MinHash / Jaccard similarity for large sets with MinHash
• learning rate / The gradient descent update rule
• locality-sensitive hashing (LSH)
  • used, for reducing pair comparisons / Reducing pair comparisons with locality-sensitive hashing
  • about / Reducing pair comparisons with locality-sensitive hashing
  • signatures, bucketing / Bucketing signatures
  • URL / Bucketing signatures
• log-linear / Visualizing the dwell times
• log-log chart / Visualizing the dwell times
• log-normal distribution
  • about / The log-normal distribution
  • correlation, visualizing / Visualizing correlation
  • jittering / Jittering
• logistic regression
  • and naive Bayes approaches, comparing / Comparing the logistic regression and naive Bayes approaches
• logistic regression, classifying
  • about / Classification with logistic regression
  • sigmoid function / The sigmoid function
  • logistic regression cost function / The logistic regression cost function
  • parameter optimization, with gradient descent / Parameter optimization with gradient descent
  • gradient descent, with Incanter / Gradient descent with Incanter
  • convexity / Convexity
• logistic regression, implementing with Incanter
  • about / Implementing logistic regression with Incanter
  • feature matrix, creating / Creating a feature matrix
  • logistic regression classifier, evaluating / Evaluating the logistic regression classifier
  • confusion matrix / The confusion matrix
  • kappa statistic / The kappa statistic
• logistic regression classifier
  • evaluating / Evaluating the logistic regression classifier
• logistic regression cost function
  • about / The logistic regression cost function
• Loom
  • used, for visualizing graphs / Visualizing graphs with Loom
  • URL / Visualizing graphs with Loom
  • graph traversal with / Graph traversal with Loom
• loss function
  • about / Ordinary least squares, Parameter optimization with gradient descent

M

• machine learning
  • movie recommendations, with ALS / Movie recommendations with alternating least squares
  • ALS, evaluating / Evaluating ALS
  • sum of squared errors, calculating / Calculating the sum of squared errors
• Mahalanobis distance measure / The Mahalanobis distance measure
• Mahout
  • URL / Large-scale clustering with Mahout
  • used, for building user-based recommenders / Building a user-based recommender with Mahout
  • used, for evaluating recommenders / Recommender evaluation with Mahout
  • Information Retrieval statistics (IR stats) evaluator / Mahout's information retrieval evaluator
• Mahout vectors
  • creating, Parkour used / Using Parkour to create Mahout vectors
  • building, from input documents / Building Mahout vectors from input documents
• matrix
  • about / Matrices
  • dimensions / Dimensions
  • vectors / Vectors
  • construction / Construction
  • scalar multiplication / Addition and scalar multiplication
  • scalar addition / Addition and scalar multiplication
  • -vector multiplication / Matrix-vector multiplication
  • -matrix multiplication / Matrix-matrix multiplication
  • transposition / Transposition
  • identity matrix / The identity matrix
  • inversion / Inversion
• matrix-matrix multiplication / Matrix-matrix multiplication
• matrix-vector multiplication / Matrix-vector multiplication
• maximum likelihood, time series
  • estimating / Maximum likelihood estimation, Estimating the maximum likelihood
  • calculating / Calculating the likelihood
  • estimating, with Nelder-Mead optimization / Nelder-Mead optimization with Apache Commons Math
  • estimating, with Akaike Information Criterion / Identifying better models with Akaike Information Criterion
• maximum likelihood estimation
  • about / Removing seasonality with differencing
• m bits / Testing set membership with Bloom filters
• mean
  • calculating, fold used / Calculating the mean using fold
• mean square error (MSE) / The F-test of model significance
• mean square model (MSM) / The F-test of model significance
• Medley
  • URL / Performing a z-test
• memoryless / The exponential distribution
• meta-algorithm / Bagging and boosting
• MinHash
  • used, for Jaccard similarity for larger sets / Jaccard similarity for large sets with MinHash
• MLlib
  • used, for large-scale machine learning / Large-scale machine learning with Apache Spark and MLlib
  • URL / Large-scale machine learning with Apache Spark and MLlib, Machine learning on Spark with MLlib
  • used, for machine learning on Spark / Machine learning on Spark with MLlib
  • using, with ALS / ALS with Spark and MLlib
  • using, with Spark / ALS with Spark and MLlib
  • ALS, evaluating / Evaluating ALS
• Monte Carlo simulation
  • used, for forecasting time series / Forecasting with Monte Carlo simulation
• Moving Average (MA) models
  • about / Moving-average models
  • autocorrelation, determining / Determining autocorrelation in MA models
  • combining, with autoregressive (AR) models / Combining the AR and MA models
• multimodal
  • about / Visualizing different populations
• multiple comparisons
  • about / Multiple comparisons
• multiple designs
  • testing / Testing multiple designs
• multiple linear regression / Multiple linear regression
• multiple tests
  • simulating / Simulating multiple tests

N

• n-gram
  • about / Better clustering with n-grams
• Naive Bayes classification
  • about / Naive Bayes classification
  • implementing / Implementing a naive Bayes classifier
  • evaluating / Evaluating the naive Bayes classifier
• natural logarithm / The log-normal distribution
• Nelder-Mead optimization
  • about / Estimating the maximum likelihood
  • with Apache Commons Math / Nelder-Mead optimization with Apache Commons Math
• network analysis
  • data, downloading / Download the data
  • data, inspecting / Inspecting the data
  • graphs, visualizing with Loom / Visualizing graphs with Loom
• new site design
  • testing / Testing a new site design
• nonresponse bias
  • about / Bias
• normal distribution
  • about / The normal distribution
• normal equation
  • about / The normal equation
  • features / More features
• null hypothesis / Hypothesis testing

O

• one-sample t-test
  • about / One-sample t-test
• one-tailed tests
  • about / Two-tailed tests
• optimal k
  • determining, with elbow method / Determining optimal k with the elbow method
  • determining, with Dunn index / Determining optimal k with the Dunn index
  • determining, with Davies-Bouldin index / Determining optimal k with the Davies-Bouldin index
• Ordinary Least Squares (OLS)
  • about / Ordinary least squares
  • slope / Slope and intercept
  • intercept / Slope and intercept
  • interpretation / Interpretation
  • visualization / Visualization
  • assumptions / Assumptions
• over-fitting
  • about / Bias and variance, Overfitting

P

• PageRank
  • used, for measuring community influence / Measuring community influence using PageRank
  • implementing, with Glittering / Implementing PageRank with Glittering
  • highest influence, sorting by / Sort by highest influence
  • running, to determine community influencers / Running PageRank to determine community influencers
• Parkour
  • URL / Parkour distributed sources and sinks
  • used, for creating Mahout vectors / Using Parkour to create Mahout vectors
• partial autocorrelation
  • calculating / Calculating partial autocorrelation
  • autocovariance / Autocovariance
  • plotting / Plotting partial autocorrelation
• partial autocorrelation function (PACF)
  • about / Calculating partial autocorrelation
  • calculating, with Durbin-Levinson recursion / PACF with Durbin-Levinson recursion
  • ARMA model order, determining / Determining ARMA model order with ACF and PACF
  • plotting, of airline data / ACF and PACF of airline data
• parts-of-speech taggers / Tokenizing the Reuters files
• Pearson's correlation
  • about / Pearson's correlation
  • sample r and population rho / Sample r and population rho
• phi-quantile / Quantiles
• Poincaré's baker
  • about / Poincaré's baker
  • distributions, generating / Generating distributions
• polytope
  • about / Estimating the maximum likelihood
• populations
  • about / Samples and populations
  • visualizing / Visualizing different populations
• precision
  • true positives / Precision
  • about / Precision
  • false positives / Precision
• prediction
  • about / Prediction
  • confidence interval / The confidence interval of a prediction
  • model, scope / Model scope
  • final model / The final model
• prediction intervals / The confidence interval of a prediction
• Pregel API
  • about / The Pregel API
  • connected components with / Connected components with the Pregel API
• probability
  • about / Probability
  • Bayes theorem / Bayes theorem
  • Bayes theorem, with multiple predictors / Bayes theorem with multiple predictors
• probability densities
  • plotting / Plotting probability densities
• probability mass function (PMF)
  • about / Probability mass functions
• processing
  • URL / Using Quil for visualization
• Pythagoras formula / The bag-of-words and Euclidean distance

Q

• quantile-quantile plots
  • about / Quantile-quantile plots
• quantiles
  • about / Quantiles
  • URL / Quantiles
• quartiles / Quantiles
• Quil, used for visualization
  • URL / Using Quil for visualization
  • about / Using Quil for visualization
  • sketch window, drawing to / Drawing to the sketch window
  • coordinate system / Quil's coordinate system
  • grid, plotting / Plotting the grid
  • fill color, specifying / Specifying the fill color
  • color and fill / Color and fill
  • image file, outputting / Outputting an image file
  • PDF, output to / Output to PDF

R

• R-squared
  • multiple / Multiple R-squared
  • adjusted / Adjusted R-squared
• random forests
  • about / Ensemble learning and random forests
• random walks
  • about / Random walks
• RDGs
  • creating, with Glittering / Creating RDGs with Glittering
• reagent
  • about / State and Reagent
• recommenders
  • evaluating, with Mahout / Recommender evaluation with Mahout
• recommenders, evaluating
  • Mahout, using / Recommender evaluation with Mahout
  • distance measures / Evaluating distance measures
  • optimum neighborhood size, determining / Determining optimum neighborhood size
  • information retrieval statistics / Information retrieval statistics
  • recommendation with Boolean preferences / Recommendation with Boolean preferences
• recommender systems
  • types / Types of recommender systems
  • collaborative filtering / Collaborative filtering
• regression
  • about / Regression
  • linear equations / Linear equations
  • residuals / Residuals
• regression lines
  • about / Regression
• relative power / Relative power
• resampling
  • about / Resampling
• residual plot / Visualization
• Resilient Distributed Datasets (RDDs) / Distributed datasets and tuples
• Reuters dataset
  • URL / Downloading the data
• Reuters documents
  • clustering / Clustering the Reuters documents
• Reuters files, tokenizing
  • about / Tokenizing the Reuters files
  • Jaccard index, applying to documents / Applying the Jaccard index to documents
  • Euclidean distance / The bag-of-words and Euclidean distance
  • bag-of-words / The bag-of-words and Euclidean distance
  • frequency vectors / Representing text as vectors
• root mean square error
  • calculating, with Parkour / Calculating the root mean square error with Parkour
• Root mean square error (RMSE) / Recommender evaluation with Mahout
• Russian election data
  • visualizing / Visualizing the Russian election data

S

• samples
  • about / Samples and populations
  • comparing / Sample comparisons
  • means, calculating / Calculating sample means
• Scalable Vector Graphics (SVG)
  • about / Scalable Vector Graphics
• scalar
  • multiplication / Addition and scalar multiplication
  • addition / Addition and scalar multiplication
• scale-free networks
  • about / Scale-free networks
• scatter plots
  • about / Scatter plots
• scatter transparency
  • about / Scatter transparency
• shortest path
  • finding / Finding the shortest path
  • minimum spanning trees / Minimum spanning trees
  • connected components / Subgraphs and connected components
  • subgraphs / Subgraphs and connected components
  • web, bow-tie structure / SCC and the bow-tie structure of the web
  • SCC / SCC and the bow-tie structure of the web
• sigmoid function / Classification with logistic regression, The sigmoid function
• significance testing
  • about / Significance
• significance testing proportions
  • about / Significance testing proportions
• simplex method
  • about / Estimating the maximum likelihood
• simulation
  • about / Introducing the simulation
  • compiling / Compile the simulation
  • browser simulation / The browser simulation
• singular matrices / Inversion
• Singular Value Decomposition (SVD) / Singular value decomposition
• skewed normal distribution / Generating distributions
• skewness
  • about / Skewness
  • quantile-quantile plots / Quantile-quantile plots
• Slope One predictors / Item-based and user-based recommenders
• Slope One recommenders
  • about / Slope One recommenders
  • URL / Slope One recommenders
  • item differences, calculating / Calculating the item differences
  • recommendations, creating / Making recommendations
• Spark
  • URL / Large-scale machine learning with Apache Spark and MLlib
  • used, for large-scale machine learning / Large-scale machine learning with Apache Spark and MLlib
• Sparkling
  • URL / Large-scale machine learning with Apache Spark and MLlib
  • used, for loading data / Loading data with Sparkling
  • used, for mapping data / Mapping data
• standard deviation / Variance
• standard error
  • about / Standard error
  • of proportion / The standard error of a proportion
  • bootstrapping, estimating with / Estimation using bootstrapping
  • of proportion, formula / The standard error of a proportion formula
• standard errors
  • adjusting, for large samples / Adjusting standard errors for large samples
• Standard Generalized Markup language (SGML) / Extracting the data
• state
  • about / State and Reagent
  • updating / Updating state
• stationary
  • about / Visualizing the airline data
• stationary time series
  • about / Stationarity
• statistics
  • sample code, downloading / Downloading the sample code
  • URL / Downloading the sample code
  • examples, running / Running the examples
  • data, downloading / Downloading the data
  • data, inspecting / Inspecting the data
  • data scrubbing / Data scrubbing
  • descriptive statistics / Descriptive statistics
• stemmers
  • URL / Stemming
• stemming
  • about / Stemming
• Stochastic gradient descent
  • about / Stochastic gradient descent
  • with Parkour / Stochastic gradient descent with Parkour
  • mapper, defining / Defining a mapper
  • shaping functions / Parkour shaping functions
  • reducer, defining / Defining a reducer
  • Hadoop jobs, specifying with Parkour graph / Specifying Hadoop jobs with Parkour graph
  • mappers, chaining with Parkour graph / Chaining mappers and reducers with Parkour graph
  • reducers, chaining with Parkour graph / Chaining mappers and reducers with Parkour graph
• stochastic gradient descent (SGD) / Stochastic gradient descent
• summary statistics
  • about / Descriptive statistics
• sum of residual squares (RSS) / The F-test of model significance
• sum of squared errors (SSE) / Calculating the root mean square error with Parkour
• supersteps / The Pregel API
• SVG maps
  • URL / Improving the clarity with illustrations

T

• t-distribution
  • about / Student's t-distribution
• t-statistic
  • about / The t-statistic
• t-test
  • performing / Performing the t-test
• Tanimoto coefficient / Recommendation with Boolean preferences
• term frequency (tf) / Representing text as vectors
• Term Frequency-Inverse Document Frequency (TF-IDF)
  • about / Better clustering with TF-IDF
  • Zipf's law / Zipf's law
  • weigh, calculating / Calculating the TF-IDF weight
  • k-means clustering with / k-means clustering with TF-IDF
  • clustering, with n-grams / Better clustering with n-grams
• term frequency vectors
  • creating / Creating term frequency vectors
  • vector space model / The vector space model and cosine distance
  • cosine distance / The vector space model and cosine distance
  • stop words, removing / Removing stop words
• Tesser
  • mathematical folds / Mathematical folds with Tesser
  • covariance, calculating with / Calculating covariance with Tesser
  • commutativity / Commutativity
  • simple linear regression / Simple linear regression with Tesser
  • correlation matrix, calculating / Calculating a correlation matrix
  • matrix-sum fold, creating / Creating a matrix-sum fold
• time series
  • Longley dataset / About the data
  • Airline dataset / About the data
  • Longley data, loading / Loading the Longley data
  • Longley data, plotting with linear model / Fitting curves with a linear model
  • decomposition / Time series decomposition
  • airline data, inspecting / Inspecting the airline data
  • airline data, visualizing / Visualizing the airline data
  • stationary time series / Stationarity
  • de-trending / De-trending and differencing
  • differencing / De-trending and differencing
  • reference link / Discrete time models
  • maximum likelihood estimation / Maximum likelihood estimation
  • forecasting / Time series forecasting
  • forecasting, with Monte Carlo simulation / Forecasting with Monte Carlo simulation
• Toeplitz matrices
  • about / PACF with Durbin-Levinson recursion
• tokenization
  • about / Set-of-words and the Jaccard index
• transduce library
  • URL / Distributed unique IDs with Hadoop
• triangle counting
  • graph density, measuring with / Measuring graph density with triangle counting
  • built-in triangle counting algorithm, running / Running the built-in triangle counting algorithm
  • implementing, with Glittering / Implement triangle counting with Glittering
  • neighbor IDs, collecting / Step one – collecting neighbor IDs
  • aggregate messages / Steps two, three, and four – aggregate messages
  • counts, dividing / Step five – dividing the counts
  • custom triangle counting algorithm, running / Running the custom triangle counting algorithm
• Twitter's intent API
  • URL / Download the data, Running PageRank to determine community influencers
• two-dimensional histogram
  • representing / Representing a two-dimensional histogram
• two-tailed tests
  • about / Two-tailed tests

U

• uberjar
  • building / Building an uberjar
  • submitting, to Hadoop / Submitting the uberjar to Hadoop
• user-based recommenders
  • about / Item-based and user-based recommenders
  • practical considerations / Practical considerations for user and item recommenders
  • building, with Mahout / Building a user-based recommender with Mahout

V

• variance
  • about / Variance
  • analysis / Analysis of variance
  • calculating, fold used / Calculating the variance using fold
• vectors
  • about / Vectors
• visualization
  • code, downloading / Download the code and data
  • data, downloading / Download the code and data
  • exploratory data visualization / Exploratory data visualization
  • two-dimensional histogram, representing / Representing a two-dimensional histogram
  • Quil, using / Using Quil for visualization
• visualization, for communication
  • about / Visualization for communication
  • wealth distribution, visualizing / Visualizing wealth distribution
  • data, bringing to life / Bringing data to life with Quil
  • bars of differing widths, drawing / Drawing bars of differing widths
  • axis labels, adding / Adding a title and axis labels
  • title, adding / Adding a title and axis labels
  • clarity, improving with illustrations / Improving the clarity with illustrations
  • text, adding to bars / Adding text to the bars
  • additional data, incorporating / Incorporating additional data
  • complex shapes, drawing / Drawing complex shapes, Drawing curves
  • compound charts, plotting / Plotting compound charts
• visualizations
  • comparative visualizations / Comparative visualizations, Comparative visualizations
  • about / The importance of visualizations
  • electorate data / Visualizing electorate data
  • comparative visualizations, of electorate data / Comparative visualizations of electorate data

W

• Waikato Environment for Knowledge Analysis (Weka)
  • URL / Classification with clj-ml
• weighted graph / Visualizing graphs with Loom
• Welch's t-test / Two-tailed tests
• whole-graph analysis
  • about / Whole-graph analysis
• Widrow-Hoff learning rule / The gradient descent update rule
• wiki
  • URL / Running the examples, Downloading the data

Z

• z-test
  • performing / Performing a z-test
• Zipf's law / Zipf's law
• Zipf scale / Scale-free networks