Comparison of Performance in Text Mining Using Text Categorization of Semi Structured Data
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
Text mining or knowledge discovery is that sub process of data mining, which is widely being used to discover hidden patterns and significant information from the huge amount of unstructured data. The enormous amount of information stored in unstructured / semi structured data cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific pre-processing methods and algorithms are required in order to extract useful patterns. In this study, we compared the performance of these classifications by applying the method of Bayesian methods, k-NN, decision trees, SVM, and as a neural network in classification on famous 20_newsgroup dataset from CMU Text Learning Group Data Archives, which has a collection of 20,000 messages, collected from 20 different net news newsgroups. The news will be classified according to their contents.
Authors and Affiliations
M. Nandhiya, Ms. M. Sakthi
Keywords
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