slugA Survey on Techniques used for Sentence Clustering of Text Documents
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 6
Abstract
Clustering techniques is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. 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 Gaussian are generally not applicable to sentence clustering. Some of the clustering algorithms are taken here for literature survey. The survey compared these methods and identified the problems in the existing systems. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to text data, and also new algorithms have recently been proposed specifically aiming at text data. This survey discuss about the different clustering algorithm and similarity measures available. Different problems of current system are also identified. Finally propose a new model for fuzzy clustering of sentence data.
Authors and Affiliations
Jinto Jacob
Keywords
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