Frequent Item-sets Based on Document Clustering Using k-means Algorithm

computer engineering

The amount of text data stored in computer repositories is growing every day, we need more than ever a reliable way to assemble or classify text documents. Clustering can provide a means of introducing some form of organization to the data, which can also serve to highlight significant patterns and trends. Document clustering is used in many fields such as data mining and information retrieval. Text clustering is the method of combining text or documents which are similar and dissimilar to one another. In several text tasks, this text mining is used such as extraction of information and concept/entity, summarization of documents, modeling of relation with entity, categorization/classification and clustering. Frequent item sets for text clustering we measure the mutual overlap of frequent sets with respect to the sets of supporting documents.

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