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OPTICS on Sequential Data: Experiments and Test Results

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International Journal of Computer Applications
© 2010 by IJCA Journal
Number 5 - Article 1
Year of Publication: 2010
Authors:
K.Santhisree
Dr A.Damodaram
10.5120/1582-2119

K.Santhisree and Dr A.Damodaram. Article:OPTICS on Sequential Data: Experiments and Test Results. International Journal of Computer Applications 11(5):1–4, December 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {K.Santhisree and Dr A.Damodaram},
	title = {Article:OPTICS on Sequential Data: Experiments and Test Results},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {5},
	pages = {1--4},
	month = {December},
	note = {Published By Foundation of Computer Science}
}

Abstract

The Web has enormous, various and knowledgeable data for data mining research. Clustering web usage data is useful to discover interesting patterns pertaining to user traversals, behaviour and their usage characteristics. Moreover, users accesses web pages in an order in which they are interested and hence incorporating sequence nature of their usage is crucial for clustering web transactions. In this paper we present OPTICS ("Ordering Points To Identify the Clustering Structure") algorithm to find density based clusters on a web usage data on MSNBC.COM website which is a free news data website with so different categories of news).The clusters are generated by OPTICS algorithm . The average of inter cluster and intra cluster are Calculated. the results are compared with different similarity measures like Euclidean , Jaccard, projected Euclidean, cosine and fuzzy similarity Finally showed behavior of clusters that made by OPTICS algorithm on a sequential data in a web usage domain. we performed a variety of experiments in the context of density based clustering , quantify our results by the way of explanation s and list conclusions.

Reference

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