Artificial Bee based Optimized Fuzzy c-Means Clustering of Gene Expression Data
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - May Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-5 | ||
| Year of Publication : 2013 | ||
| Authors :Punam Priti Pradhan, Debahuti Mishra, Sashikala Mishra and Kailash Shaw |
Punam Priti Pradhan, Debahuti Mishra, Sashikala Mishra and Kailash Shaw"Artificial Bee based Optimized Fuzzy c-Means Clustering of Gene Expression Data "International Journal of Computer Trends and Technology (IJCTT),V4(5):1-5 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - Artificial Bee Colony (ABC) algorithm is a swarm based meta-heuristic algorithm that was introduced by Karaboge in 2005 for optimizing numerical problem. Clustering is an important tool for a variety of applications in data mining, statistical data analysis, data compression and vector quantization. The goal of clustering is to organize data into clusters such that the data in each cluster shares a high similarity while being very dissimilar to data from other clusters. Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. In this paper, we have used the ABC fuzzy clustering on three different data sets from UCI database. Here we show how ABC optimization algorithm is successful in fuzzy c-means clustering.
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Keywords — Artificial bee colony, Data normalization, Principal component analysis, Fuzzy c-means