article Open Access
Effect of changing the number of parameters in a dataset on the result of k-means clustering algorithm
International Journal of Statistics and Applied Mathematics · 2019 · Vol. 4(3) · pp. 43–46
Abstract
One of the main analytical techniques in data mining nowadays is clustering analysis. Clustering analysis is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. One of the most common clustering algorithms is the k-means algorithm. This paper is a study of effect of changing the number of parameters in a dataset on the result of k-means clustering algorithm.. Experimental results show that there is a considerable saving in runtime without affecting the results if the input data points are appropriately chosen.
Advanced Data Processing TechniquesData Mining Algorithms and ApplicationsCluster analysisCorrelation clusteringData miningCURE data clustering algorithmComputer scienceSet (abstract data type)Task (project management)Canopy clustering algorithmData stream clusteringAlgorithm
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