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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Introducing DBSCAN

The basic idea behind the density-based spatial clustering of applications with noise (DBSCAN) algorithm is that clusters are regions of high point density, separated from other clusters by low point density regions. The algorithm takes each point in the dataset to identify the high-density regions and checks whether its neighborhood contains a minimum number of points. Unlike K-means, DBSCAN does not require manually specifying the number of clusters; it is more immune to outliers and more appropriate when the clusters have complex shapes.

To employ the algorithm, we need to set two hyperparameters:

  • epsilon is the radius of the circle to be created around each point to check the region’s density
  • minPts determines the minimum number of data points within the circle to label its center as a core point

All the data points with less than minPts but more than one point in their neighborhood are called border points. Finally, data points without...

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