Improved Ant Colony Optimization in K-Means for Data Clustering

Autor:	A. J. Umbarkar, S. S. Bamane, M. R. Gaikwad
Rok vydání:	2020
Předmět:	Computer science business.industry Ant colony optimization algorithms Big data k-means clustering Evolutionary algorithm Centroid computer.software_genre Data point Segmentation Data mining business Cluster analysis computer
Zdroj:	ICT Analysis and Applications ISBN: 9789811506291
DOI:	10.1007/978-981-15-0630-7_52
Popis:	Clustering is grouping of similar data points in clusters. Clustering has many applications, particularly in big data analytics. In data mining, traditional algorithm are used for clustering. These algorithms are inefficient in terms quality of cluster. This paper attempts to improvise the traditional K-mean by adding the Ant Colony Optimization algorithm (ACO) for improving the centroid for better clustering. This combination of ACO in K-mean and IACO in K-mean is experimented on iris and skin segmentation supervised datasets. Experimental results show that the performance in terms of F-measure for IACO in K-mean is better than ACO in K-mean and traditional K-means for iris and skin segmentation datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a79227228b754bf334e386a5dc451f0c https://doi.org/10.1007/978-981-15-0630-7_52 Zobrazit plný text záznamu