An Operative Algorithm for K-Means Clustering with New Initial Centroids

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Organizing data into sensible groups is the most fundamental way of understanding and learning. Clustering helps to organize data based on natural grouping without any category labels to identify the clusters. One of the most popular and simplest partitional clustering algorithms is the K-Means published in 1955. K-Means algorithm is computationally expensive and the final clusters depend entirely on the initial selection of centroids. This method also insists the selection of number of clusters initially. Several modifications have been proposed for the K-Means clustering method. Some such proposals are summarized and reviewed. A new method is proposed considering both the standard deviation and mean of the attributes of the dataset.
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