Friday 29th of March 2024
 

FLoMSqueezer: An Effective Approach For Clustering Categorical Data Stream


Marpe Sora, Swarup Roy and S I Singh

Squeezer is an effective histogram based approach for categorical data stream clustering. Drawback of Squeezer is that it is not scalable in terms of memory. The size of histogram increases with the increase in records in the dataset. Accommodation of unpredictably large histogram in the main memory is not always feasible. To handle the bottleneck, a modified version of Squzeer, FLoMSqueezer, is proposed in this paper. It uses concise sampling technique for handling increasing memory requirement by the Squzeer. Experimental results shows that proposed approach scales better in terms of quantitative cluster, memory as well as execution time.

Keywords: Cluster analysis, data stream, histogram, sampling, quantitative cluster.

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ABOUT THE AUTHORS

Marpe Sora
obtained B.Tech form NERIST and M.Tech from Tezpur University. Presently he is Assistant professor in Rajiv Gandhi University, Department of Computer Science and Engineering Arunachal Pradesh. His main research interests include signal and speech processing and Data mining.

Swarup Roy
did his M.Tech. in Information Technology and pursuing his Ph.D in Comp Sc & Engg. from Tezpur University. Presently he is an Assistant Professor in the department of Information Technology at North Eastern Hill University, Shillong. He is a recipient of university gold medal for securing first position in M.Tech. His research interest includes Data mining and Computational Biology. S Roy has published a number of papers in different International Journals and refereed Int’l. Conf. Proceedings and authored a book. He is a reviewer of few International Journals.

S I Singh
obtained MCA from Manipur University. He is presently working as Assistant Professor in the Department of Computer Science & Engineering at Tezpur University. His current area of interest is Data mining, Spatial Database and Web Services.


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