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Hands-On Big Data Modeling

You're reading from   Hands-On Big Data Modeling Effective database design techniques for data architects and business intelligence professionals

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788620901
Length 306 pages
Edition 1st Edition
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Authors (3):
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James Lee James Lee
Author Profile Icon James Lee
James Lee
Tao Wei Tao Wei
Author Profile Icon Tao Wei
Tao Wei
Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
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Toc

Table of Contents (17) Chapters Close

Preface 1. Introduction to Big Data and Data Management 2. Data Modeling and Management Platforms FREE CHAPTER 3. Defining Data Models 4. Categorizing Data Models 5. Structures of Data Models 6. Modeling Structured Data 7. Modeling with Unstructured Data 8. Modeling with Streaming Data 9. Streaming Sensor Data 10. Concept and Approaches of Big-Data Management 11. DBMS to BDMS 12. Modeling Bitcoin Data Points with Python 13. Modeling Twitter Feeds Using Python 14. Modeling Weather Data Points with Python 15. Modeling IMDb Data Points with Python 16. Other Books You May Enjoy

Theory

Clustering is the machine learning task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Given a set of data points, we can use a clustering algorithm to group each data point into a specific group. In theory, data points that are clustered in the same group should have similar properties or features, while data points in different groups should have highly distinct properties or features. Clustering is a common technique for statistical data analysis, and is used in many fields.

There are different types of clustering algorithm. The following are the most common clustering algorithms:

  • K-means clustering algorithm
  • Mean-shift clustering
  • Agglomerative-hierarchical clustering
  • Density-Based Spatial Clustering

We use clustering for IMDb because similar datasets are very close to each other...

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