Manuscript Number : CSEIT1831178
Hybrid Data Cost Setting using K-Means & ACO to Optimize Data Cost
Authors(2) :-Anita Bishnoi, Mr. Vinod Todwal In k-means clustering, we are given a set of n data points in d-dimensional space Rd and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A widespread heuristic for k-means clustering is Lloyd's algorithm. In this paper, we present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure .We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.
Anita Bishnoi ACO, Clusters, K-means, Mahalanobis Distance. Publication Details Published in : Volume 3 | Issue 1 | January-February 2018 Article Preview
Rajasthan College of Engineering for Women, Jaipur, Rajasthan, India
Mr. Vinod Todwal
Rajasthan College of Engineering for Women, Jaipur, Rajasthan, India
Date of Publication : 2018-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 755-760
Manuscript Number : CSEIT1831178
Publisher : Technoscience Academy