Knowledge exploration from the large set of data, generated as a result of the various data processing activities is an effective application of data mining. Text mining applications have also become important areas of application in the field of document processing. Document clustering has also become an important process for helping the information retrieval systems to organize vast amount of data. This can be tried with categorical data and for image categorization. At the same, time, frequent pattern mining has also become a very important undertaking in data mining. In the research work described in this paper, Apriori algorithm has been applied to generate frequent itemset and this method contains mainly two steps, viz. candidate generation and pruning techniques for the satisfaction of the desired objective. Aim of this paper is to focus on frequent itemset generation from dataset of variable length. Several steps have been executed to achieve the desired result. The primary goal has been to build a method which can be used to find significant items from a text database in an easy and efficient way.

Itemsets, Tokenization, Stemming, Apriori algorithm

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