抽象的な

Development of Fuzzy based categorical Text Clustering Algorithm for Information Retrieval

S.M. Jagatheesan, V. Thiagarasu

Similarities play a vital role in clustering text on the prediction, in order to produce an efficient result when compared to the existing algorithms like k-modes, ROCK and STIRR. Future selection is important for making a subset according to the dataset. In order to overcome the problems in the existing system, single cluster and multiple clustering methods are proposed in order to cluster the famous quotes with multiple semantic associations. But the problems on overlapping between the quotes are analyzed and the sentence similarities for information retrieval are measured. A FUZZY logic in finding the similarities to form a cluster, based on the relational prototypes has been proposed. A semantic clustering and FUZZY based pruning approach is practiced to bring more accuracy in mining process. FUZZY makes possible on using more complex prototypes that should be represent on the clustered text. The algorithm identifies the semantically related sentences and avoids duplication on the given data set. The information retrieval based on the keyword in which filtering is processed on the benchmark dataset. The result states the information retrieval based on the FUZZY algorithm maximizes the effectiveness.