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The steps involved in text mining are as follows: 1.Collecting unstructured data from various data sources like web pages, plain text, pdf files, and blogs, etc. 2.Detecting and eliminating anomalies from data collected through pre-processing and cleansing. (Data cleansing lets you to extract and retain the crucial information hidden within the data). 3.Converting the relevant information extracted into structured formats. 4.Analysing the patterns within the data via MIS. 5.Storing valuable information into a secure database.

Text categorisation is nothing but a form of supervised learning where normal language texts depending on their content are assigned to a predefined set of topics. Thus, categorisation is a process of collecting text documents, processing and analysing them to uncover the right indexes for each document. The co-referencing approach is commonly used as a part of NLP to extract suitable abbreviations and synonyms from textual data.

Tarushi, clustering is one of the most popular techniques of text mining. It identifies intrinsic structures in textual information and organises them into relevant clusters for further analysis. A challenge faced in the clustering process is to form meaningful subgroups from the unlabeled textual data without having prior information on them. Cluster analysis acts as a pre-processing step for other text mining algorithms.

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