Clustering is a process of partitioning a set of data into a set of meaningful sub-classes, called clusters. K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, the proposed algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed clustering algorithm over existing K-means clustering algorithm in terms of computational complexity and overall performance.
Title = "Efficient K-Means Clustering Algorithm Using Feature Weight and Min-Max Normalization",
Journal ="International Journal of Science and Engineering Applications (IJSEA)",
Volume = "7",
Pages ="479 - 503",
Year = "2018",
Authors ="Ei Ei Phyo , Ei Ei Myat"}