Designing a Knowledge Discovery of Clustering Techniques in Pharmaceutical Compounds
Keywords:
Enhanced K-Mean algorithm, Chameleon algorithm, Birch algorithmAbstract
To develop data mining techniques to support decision making and discovery of functional group of the connectivity atom for drug effects by analyzing chemical compound data in the form of structured data. Existing studies in data mining mostly focus on hierarchical clustering techniques applied in large and small dataset of pharmaceutical compound and analyse its performance based on time accuracy. In this paper focuses to apply cluster techniques of partition method like Enhanced K-means algorithm and hierarchical method like Birch and Chameleon algorithm used in pharmaceutical compound specifically represented as atom number, atom name like carbon, hydrogen, nitrogen, oxygen with connected atoms. These dataset form a functional group of atoms by functioning in three phases. The performance can be experimented based on time taken to form the estimated cluster, also overall execution time can be reduced by improvement of Enhanced Kmeans algorithm when compared to chameleon and Birch algorithm.
References
Fahim A.M, Salem A. M, Torkey A and Ramadan M. A, “An Efficient enhanced k-means clustering algorithm,” Journal of Zhejiang University, 10(7):1626–1633, 2006.
Yuan F, Meng Z. H, Zhang H. X and Dong C. R, “A New Algorithm to Get the Initial Centroids,” Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pages 26–29, August 2004.
Daxin Jiang, Chum Tong and Aidong Zhang, “Cluster Analysis for Gene Expression Data,” IEEE Transactions on Data and Knowledge Engineering, 16(11): 1370-1386, 2004.
V. Palanisamy, A. Kumarkombaiya, “Analysing Pharmaceutical Compounds Based On Cluster Techniques”, International Journal of Computer Science Research & Technology, ISSN: 2321-8827, Vol. 1 (03).
Jiawei Han and Micheline Kamber,” Data Mining: Concepts and Techniques”. Publication: ISBN-10: 0123814790 | ISBN-13: 9780123814791, Edition: 3
Zhang, R. Ramakrishnan and M. Livny: BIRCH : “An Efficient Data Clustering Method for Very Large Databases”. SIGMOD “96 6/96 Montreal, CanadaIQ1996ACM0-89791-794-4/96/0006.
Daniel T. Larose , Data Mining Methods and Models, Copyright © 2006 John Wiley and Sons, Inc.
Marjan Kuchaki Rafsanjani, Zahra Asghari Varzaneh Nasibeh Emami Chukanlo, “A survey of hierarchical clustering algorithms”, The Journal of Mathematics and Computer Science Vol .5 No.3 (2012) 229-240.
M. R. Anderberg; Cluster Analysis for Applications: Academic Press, New York, 1973.
D. Pelleg and A. Moore; X-means: Extending k-means with efficient estimation of the number of clusters: In Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, pp. 727- 734, 2000.
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and AngelaY. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans.Pattern Anal. Mach. Intell., 24(7):881–892, 2002.
Yuan F, Meng Z. H, Zhang H. X and Dong C. R, “A New Algorithm to Get the Initial Centroids,” Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pages 26–29, August 2004.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
