Description

In comparison with hard clustering methods, in which a pattern belongs to a single cluster, fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of membership. This is important in domains such as sentence clustering, since a sentence is likely to be related to more than one theme or topic present within a document or set of documents. However, because most sentence similarity measures do not represent sentences in a common metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of Caussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy clustering algorithm that operates on relational input data; i.e., data in the form of a square matrix of pair wise similarities between data objects In comparison with hard and soft clustering methods, in which a pattern belongs to a single cluster, fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of membership. In Existing a novel fuzzy clustering algorithm that operates on relational input data. Results of applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping clusters of semantically related sentences, and that it is therefore of potential use in a variety of text mining tasks. The system also includes results of applying the algorithm to benchmark data sets in several other domains. Sentence clustering plays an important role in many text processing activities. In early classic summarization system, the important summaries were created according to the most frequent words in the text.