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Reseach Article

Article:OPTICS on Sequential Data: Experiments and Test Results

by K.Santhisree, Dr A.Damodaram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 5
Year of Publication: 2010
Authors: K.Santhisree, Dr A.Damodaram
10.5120/1582-2119

K.Santhisree, Dr A.Damodaram . Article:OPTICS on Sequential Data: Experiments and Test Results. International Journal of Computer Applications. 11, 5 ( December 2010), 1-4. DOI=10.5120/1582-2119

@article{ 10.5120/1582-2119,
author = { K.Santhisree, Dr A.Damodaram },
title = { Article:OPTICS on Sequential Data: Experiments and Test Results },
journal = { International Journal of Computer Applications },
issue_date = { December 2010 },
volume = { 11 },
number = { 5 },
month = { December },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume11/number5/1582-2119/ },
doi = { 10.5120/1582-2119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:59:47.247078+05:30
%A K.Santhisree
%A Dr A.Damodaram
%T Article:OPTICS on Sequential Data: Experiments and Test Results
%J International Journal of Computer Applications
%@ 0975-8887
%V 11
%N 5
%P 1-4
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
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.

References
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  6. Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise”. In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proc. 2 International Conference on Knowledge Discovery and Data Mining (KDD-96). pp.226-231.
  7. “Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander”, “OPTICS: Ordering Points To Identify the Clustering Structure” Proc. ACM SIGMOD’99 Int. Conf. on Management of Data, Philadelphia PA, 1999.
  8. Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara and Sandhya Dwarkadas, “Incremental and Interactive Sequence Mining”. Proc. in 8th ACM International Conference Information and Knowledge Management. Nov 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering algorithm OPTICS Ordering Points To Identify the Clustering Structure Sequence mining Average Inter cluster Intra cluster