期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：5-4
出版社：Seventh Sense Research Group
摘要：Artificial Bee Colony (ABC) algorithm is a swarm based metaheuristic 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 cmeans (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 cmeans clustering.
关键词：Artificial bee colony; Data normalization; Principal component analysis; Fuzzy c-means
