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Case Study : Comparative Analysis: On Clustering of Sequential Data Streams USING Optics and ICA
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 135 - Number 2 |
| Year of Publication: 2016 |
| Authors: K. Santhi Sree, R. Kranthi Kumar |
10.5120/ijca2016908405
|
K. Santhi Sree, R. Kranthi Kumar . Case Study : Comparative Analysis: On Clustering of Sequential Data Streams USING Optics and ICA. International Journal of Computer Applications. 135, 2 ( February 2016), 34-37. DOI=10.5120/ijca2016908405
Abstract
Clustering on web usage data is useful to identify what users are exactly looking for on the world wide web, lik e user traversals, users behavior and their characteristics, which helps for Web personalization. Clustering web sessions is to group them based on similarity and consists of minimizing the Intra-cluster similarity and maximizing the Inter-group similarity. In the past there exist multiple similarity measures like Euclidean, Jaccard ,Cosine ,Manhanttan, Minkowski, and many to measure similarity between web patterns. In this paper, we enhanced Icremental clustering algorithm (ICA) based on OPTICS. Experiments are performed on MSNBC.COM website ( free online news channel), on sequential data streams in the context of clustering in the domain of Web usage mining. Specially, we present a detailed comparison of ICA and OPTICS and the results illustrate that ICA is much more suitable for clustering the dynamic datasets. The Inter-cluster and Intra-cluster distances are computed using Average Levensthien distance (ALD) to demonstrate the usefulness of the proposed approach in the context of web usage mining. This new enhanced (ICA algorithm )has good results when compared with existing OPTICS clustering technique , and provided good time requirements of the newly developed algorithms.
References
- Aggarwal.C, Han.J, Wang.J, Yu.P.S, “A Framework for Projected Clustering of High Dimensional Data Streams”, Proc. 2004 Int. Conf. on Very Large Data Bases, Toronto, Canada, pp.(852-863), 2004.
- Aoying.Z, Shuigeng.Z, “Approaches for scaling DBSCAN algorithm to large spatial database”, Journal of Computer Science and Technology, Vol 15(6), pp.(509–526), 2000.
- Chen Song-Yu, O'Grady2,O'Hare, Wei Wang, “A Clustering Algorithm Incorporating Density and Direction”, IAWTAC ,IEEE 2008.Deepak P, Shourya Roy IBM India Research Lab, OPTICS on Text Data: Experiments and Test Results.
- Cooley.R,Mobasher. B,Srivastava.J, “Web mining: Information and pattern discovery on the world wide web”, 9th IEEE Int. Conf. Tools AI .
- Guha.s, Mishra.n, Motwani.r, Callaghan.l,“ Clustering data streams”. In Proceedings of Computer Science. IEEE,,November Vol.16(10),pp(1391-1399),2000.
- K.Santhisree, Dr A.Damodaram, ‘SSM-DBSCAN and SSM-OPTICS : Incorporating a new similarity measure for Density based Clustering of Web usage data”. International Journal on Computer Science and Engineering (IJCSE),Vol.3(9),PP.(3170-3184)September 2011,India.
- K.Santhisree,” SSM-DENCLUE : Enhanced Approach for Clustering of Sequential data: Experiments and Test cases, International Journal of Computer Applications,Vol.96(6),pp.(7-14),June 2014. Published by Foundation of Computer Science, New York, USA.
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