title,doi,url,abstract,journal,publication_year,pmid,arxiv
Hybrid Data Cost Setting using K-Means & ACO to Optimize Data Cost , https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT1831178, 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., International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2018, CSEIT1831178