CFP last date
22 April 2024
Reseach Article

Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling

by Rajnish Dashora, Harsh Bajaj, Akshat Dube, Geetha Mary. A
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 8
Year of Publication: 2013
Authors: Rajnish Dashora, Harsh Bajaj, Akshat Dube, Geetha Mary. A
10.5120/13760-1600

Rajnish Dashora, Harsh Bajaj, Akshat Dube, Geetha Mary. A . Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling. International Journal of Computer Applications. 79, 8 ( October 2013), 11-17. DOI=10.5120/13760-1600

@article{ 10.5120/13760-1600,
author = { Rajnish Dashora, Harsh Bajaj, Akshat Dube, Geetha Mary. A },
title = { Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 8 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number8/13760-1600/ },
doi = { 10.5120/13760-1600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:28.389441+05:30
%A Rajnish Dashora
%A Harsh Bajaj
%A Akshat Dube
%A Geetha Mary. A
%T Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 8
%P 11-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increasing size of data-sets in application areas like bio-medical, hospitals, information systems, scientific data processing and predictions, finance analytics, communications, retail and marketing, it is becoming increasingly important to execute data mining tasks in parallel. At the same time, technological advancements have made shared memory-parallel computation machines commonly available to various organizations and individuals. This paper analyzes a hierarchical clustering algorithm named chameleon clustering which is based on dynamic modeling and we propose a parallel algorithm for the same. The algorithm utilizes the concept of parallel processors available and hence reduces the time to generate final clusters.

References
  1. Hadjidoukas, P. E. & Amsaleg, L. Parallelization of a Hierarchical Data Clustering Algorithm Using OpenMP In Proc. the 2nd International Workshop on OpenMP (IWOMP '06, 2006)
  2. Garcia, V. ; Debreuve, E. & Barlaud, M. Fast k-nearestneighbor search using GPU Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on, 2008, 1-6
  3. Karypis, G. ; Han, E. -H. (S. & Kumar, V. Chameleon: Hierarchical Clustering Using Dynamic Modeling Computer, IEEE Computer Society Press, 1999, 32, 68-75
  4. Sismanis, N. ; Pitsianis, N. & Sun, X. Parallel search of k-nearest neighbors with synchronous operations. High Performance Extreme Computing (HPEC), 2012 IEEE Conference on, 2012, 1-6
  5. Xu, R. & Wunsch D. , I. Survey of clustering algorithms Neural Networks, IEEE Transactions on, 2005, 16, 645-678
  6. Maitrey, S. ; Jha, C. K. ; Gupta, R. & Singh, J. Article: Enhancement of CURE Clustering Technique in Data Mining. IJCA Proceedings on Development of Reliable Information Systems, Techniques and Related Issues (DRISTI 2012), 2012, DRISTI, 7-11
  7. J. Han and M. Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufmann. 2000
  8. Karypis, G. & Kumar, V. Parallel Multilevel Graph Partitioning Proceedings of the 10th International Parallel Processing Symposium, IEEE Computer Society, 1996, 314-319
  9. Graph Partitioning Algorithms for Distributing Workloads of Parallel Computations (generals exam). Bradford L. Chamberlain. University of Washington Technical Report UW-CSE-98-10-03, October 1998.
  10. Foti, D. ; Lipari, D. ; Pizzuti, C. & Talia, D. Scalable Parallel Clustering for Data Mining on Multicomputers Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, Springer-Verlag, 2000, 390-398.
  11. K. P. Soman, Shyam Diwakar, V. Ajay, InsightInto Data Mining: Theory and Practice, PHI Learning Pvt Ltd, 2006.
  12. Xu, X. ; Jäger, J. & Kriegel; H. -P. A Fast Parallel Clustering Algorithm for Large Spatial Databases Data Min. Knowl. Discov. , Kluwer Academic Publishers, 1999, 3, 263-290.
  13. George Karypis and Vipin Kumar A Hypergraph Partitioning Package Version 1. 5. 3. Army HPC Research Center. November 22, 1998
  14. https://developer. nvidia. com/cublas
  15. Guha, S. ; Rastogi, R. & Shim, K. CURE: an efficient clustering algorithm for large databases Proceedings of the 1998 ACM SIGMOD international conference on Management of data, ACM, 1
Index Terms

Computer Science
Information Sciences

Keywords

Multicore Processors Data Mining Cluster analysis Hierarchical Clustering Chameleon Data points Shared Memory Symmetric Multiprocessing(SMP) Dynamic Modeling ParMetis