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Scala Machine Learning Projects

You're reading from   Scala Machine Learning Projects Build real-world machine learning and deep learning projects with Scala

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788479042
Length 470 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Analyzing Insurance Severity Claims FREE CHAPTER 2. Analyzing and Predicting Telecommunication Churn 3. High Frequency Bitcoin Price Prediction from Historical and Live Data 4. Population-Scale Clustering and Ethnicity Prediction 5. Topic Modeling - A Better Insight into Large-Scale Texts 6. Developing Model-based Movie Recommendation Engines 7. Options Trading Using Q-learning and Scala Play Framework 8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks 9. Fraud Analytics Using Autoencoders and Anomaly Detection 10. Human Activity Recognition using Recurrent Neural Networks 11. Image Classification using Convolutional Neural Networks 12. Other Books You May Enjoy

Other topic models versus the scalability of LDA

Throughout this end-to-end project, we have used LDA, which is one of the most popular TM algorithms used for text mining. We could use more robust TM algorithms, such as Probabilistic Latent Sentiment Analysis (pLSA), Pachinko Allocation Model (PAM), and Hierarchical Drichilet Process (HDP) algorithms.

However, pLSA has the overfitting problem. On the other hand, both HDP and PAM are more complex TM algorithms used for complex text mining, such as mining topics from high-dimensional text data or documents of unstructured text. Finally, non-negative matrix factorization is another way to find topics in a collection of documents. Irrespective of the approach, the output of all the TM algorithms is a list of topics with associated clusters of words.

The previous example shows how to perform TM using the LDA algorithm as a standalone...

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