Document Clustering using Python

Abstract

The grouping in classes of similar objects is called the clustering process of a set of physical or abstract objects. The most common type of supervised learning is clustering. For unattended organization of document, automatic extraction of the topic and quick information recall, clustering documents is more specific technique. Rapid, high-quality document clustering algorithms helps users navigate, summarize and organize information effectively. Every day, from the extremes of large or minor portals around the world, we find a large number of documents. The K mean algorithm is the partial clustering algorithm most commonly used. The present work is directed to cluster documents using Self-Organizing Map (SOM), FUZZY C Meaning and K means algorithms. The aim of this study is to divide and data sets into K clusters with a closest significance (Similarity).