Topic Oriented Probability Based and Semi Supervised Document Clustering

Abstract

Clustering of related or similar objects has long been regarded as a potentially useful contribution for helping users to navigate an information space such as a document collection. But, the major challenge in document clustering is high dimensionality. Data mining and statistical techniques have been applied with some success to large set of documents to automatically produce meaningful subsets. Many clustering algorithms and techniques have been developed and implemented since the earliest days of computational information retrieval but as the sizes of document collections have grown, these techniques have not been scaled to large collections because of their computational overhead. Traditional document clustering is usually considered as an unsupervised learning. It cannot effectively group documents under user’s need. To solve this problem, the proposed system concentrates on an interactive text clustering methodology, topic oriented probability based and semi supervised document clustering. It suggests interactive approach for document clustering, to facilitate human refinement of clustering outputs. The proposed system evaluates system efficiency by implementing and testing the clustering results with Dbscan and K-means clustering algorithms. Experiment shows that the proposed document clustering algorithm performs with an average efficiency of 94.4% for various document categories.