Text Analysis with Python: A Research Oriented Guide

Author(s): ​Mamta Mittal, ​Gopi Battineni, Bhimavarapu Usharani and Lalit Mohan Goyal

DOI: 10.2174/9789815049602122010007

Text Clustering in Python

Pp: 121-158 (38)

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Abstract

SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.

Abstract

In this chapter, we learn about clustering and how document and text clustering can be performed. This chapter explains the real-time applications of text clustering and the differences between soft and hard clustering types. The clustering algorithms, including KNN, hierarchical and Fuzzy clustering, were used . Fuzzy clustering or soft clustering types can add better value performance-wise than the other two clustering algorithms. Besides, we also presented how to conduct text clustering in python using unsupervised machine learning techniques. To explain this in detail, the IRIS dataset is considered famous in UCI Machine learning Repository and well presented with python script.

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