K-means with Three different Distance Metrics

作者：Archana Singh, Avantika Yadav, Ajay Rana · 发表于：International Journal of Computer Applications · 年份：2013 · DOI：10.5120/11430-6785 · 被引用次数：381 · 研究领域：Algorithms and Data Compression、Data Mining Algorithms and Applications、Data Management and Algorithms

The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used.Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering.In this paper, the results obtained by implementing the k-means algorithm using three different metrics Euclidean, Manhattan and Minkowski distance metrics along with the comparative study of results of basic k-means algorithm which is implemented through Euclidian distance metric for twodimensional data, are discussed.Results are displayed with the help of histograms.