An Adaptive Hierarchical Clustering Algorithm for Segmenting Sentence level Text

International Journal of Computer & Organization Trends  (IJCOT)          
© 2014 by IJCOT Journal
Volume - 4 Issue - 6
Year of Publication : 2014
Authors : Gandikota Gopi , Mrs.T.Suneetha Rani
DOI : 10.14445/22492593/IJCOT-V15P310


Gandikota Gopi , Mrs.T.Suneetha Rani "An Adaptive Hierarchical Clustering Algorithm for Segmenting Sentence level Text", International Journal of Computer & organization Trends (IJCOT), V4(6):23-27 Nov - Dec 2014, ISSN:2249-2593, Published by Seventh Sense Research Group.


Text segmentation is designed to group documents with high levels of similarity. It has found applications in several fields of text mining and data retrieval. The digital data accessible nowadays has steadily grown in tremendous volume and retrieving useful information from that is quite challenge. Text clustering has discovered an important usage to organize information and to extract useful information from the available corpus. In this proposed system , we have method for clustering the text documents. In the initial phase characteristics are choosen using a preprocessing based method. In the next phase the extracted keywords are clustered by means of hybrid algorithm.In this proosed work, graph based Fuzzy EM framework is implemented to cluster the sentences with the corpus. Experimental results show proposed approach has better performance in terms of cluster rate and time are concern


[1] V. Hatzivassiloglou, J.L. Klavans, M.L. Holcombe, R. Barzilay, M. Kan, and K.R. McKeown, “SIMFINDER: A Flexible Clustering Tool for Summarization,” Proc. NAACL Workshop Automatic Summarization, pp. 41-49, 2001.
[2] H. Zha, “Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering,” Proc. 25th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 113-120, 2002.
[3] D.R. Radev, H. Jing, M. Stys, and D. Tam, “Centroid-Based Summarization of Multiple Documents,” Information Processing and Management: An Int’l J., vol. 40, pp. 919-938, 2004.
[4] R.M. Aliguyev, “A New Sentence Similarity Measure and Sentence Based Extractive Technique for Automatic Text Summarization,” Expert Systems with Applications, vol. 36, pp. 7764-7772, 2009.
[5] R. Kosala and H. Blockeel, “Web Mining Research: A Survey,” ACM SIGKDD Explorations Newsletter, vol. 2, no. 1, pp. 1-15, 2000.
[6] Ms. Seema V. Wazarkar, Ms. Amrita A. Manjrekar, “Text Clustering Using HFRECCA and Rough K-Means Clustering Algorithm”, International Conference on Advances in Computer Engineering & Applications (ICACEA-2014) at IMSEC, GZB.
[7] K.Sathishkumar, E.Balamurugan, and D.Kavin , ”Sentence Level Clustering Approaches and its Issues in Various Applications”, International Journal of Applied Research “