Text Document Featured Clustering and Classification using Fuzzy Logic
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Abstract
Recent years huge amount of information are available in the World Wide web. ‘Text Document Featured Clustering and Classification Using Fuzzy Logic’ aims at obtaining effective clustering of text documents which can simply browsing of large volume of data set using semantic relationship between the words. Text clustering is mainly used for clustering a set of documents based on the user typed key term. ‘A Fuzzy similarity-based algorithm’ automatically classify a group of documents into set of groups with frequent concept. The set of documents and key terms which are entered by the user are pre-processed. The proposed system uses Fuzzy Featured Clustering(FFC) algorithm to identify the semantic relationship of words to create concepts. Text is classified based on features into number of clusters. The frequency of words are weighted by weighted matrix. The relationship between words like synonyms, hypernyms, hyponyms can also be identified. The proposed system is found more accurate, scalable and effective when compared to existing text clustering algorithms.
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Keywords: Data Mining, Fuzzy Featured Clustering, Text Clustering, Classification, Fuzzy Logic
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