A tutorial review on Text Mining Algorithms

Abstract: As we enter the third decade of the World Wide Web (WWW), the textual revolution has seen a tremendous change in the availability of online information. Finding information for just about any need has never been more automatic—just a keystroke or mouse click away .It can be viewed as one of a class of non traditional Information Retrieval (IR) strategies which attempt to treat entire text collections holistically, avoid the bias of human queries, objectify the IR process with principled algorithms, and "let the data speak for itself." These strategies share many techniques such as semantic parsing and statistical clustering, and the boundaries between them are fuzzy. In this paper different existing Text Mining Algorithms i.e Classification Algorithm, Association Algorithm, Clustering Algorithm is briefly reviewed, stating the merits / demerits of the algorithms. In addition some alternate implementation of the algorithms is proposed. Finally the logic of these algorithms are , merged to generate an algorithm which will perform the task of Classification of a data set into some predefined classes, establish relationship between the classified date and finally cluster the data based on the association between them into groups.

Keywords: Data Mining, Text Mining, Classification, Clustering, Association, Agglomerative, Divisive, Information Retrieval, Information Extraction.