Application of Fuzzy K-Means (FKM) Algorithms in Identifying Better Clusters of Few Drugs from Drugbank Database
Naga Madhavi Latha Kakarla1, G.Rama Mohan Babu2
1Naga Madhavi Latha Kakarla, Research Scholar, Department of Computer Science and Engineering, University College of Engineering & Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Andhra Pradesh, India.
2G. Rama Mohan Babu, Professor, Department of Information Technology, R.V.R. & J.C College of Engineering, Chowdavaram, Andhra Pradesh, India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 655-660 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F11340486S419/19©BEIESP | DOI: 10.35940/ijitee.F1134.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The fuzzification of the cluster configuration is refereed as Fuzzy K-Means (FKM) where the algorithm generates limited homogeneous clusters. The data points are assigned respective clusters in accordance to the membership degrees within interval [0,1]. Several variations of FKM algorithm were applied in identifying better clusters of few drugs data set derived from Drug Bank database as possible GSK-3 beta inhibitors defined against diabetes. Better clusters were evaluated based on cluster balance and membership degree plots. With k=3, observation of cluster balance and membership degree plots revealed that FKM with entropy is the best method of choice with equal assignment of objects and no ambiguous assignments. The membership degree plot resulted in a good fuzzy clustering result where only 3 points appeared between membership degrees of 0.6 to 0.8.
Keywords: Fuzzy k-Means, FKM, Clustering, GSK-3 beta, Cluster Balance, Membership Degree.
Scope of the Article: Fuzzy Logics