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۱Improved K–Means Clustering Algorithm by Getting Initial Cenroids
نویسنده(ها): ، ،
اطلاعات انتشار: World Applied Sciences Journal، بيست و هفتم،شماره۴، ۲۰۱۳، سال
تعداد صفحات: ۹
To extract useful information from huge data sets are considerable problem for researcher that is insufficient for conventional databases querying methods. K–means clustering algorithm is a one of the major cluster analysis method that is commonly used in practical applications for extracting useful information in terms of grouping data. But the standard K–means algorithm is computationally expensive by getting centroids that provide the quality of the clusters in results. For improving the performance of the K means clustering algorithm, several methods have been proposed that are described in literature review. This paper proposes a method for effective clustering by selecting initial centroids. Firstly, this algorithm evaluate the distance between data points according to criteria; then try to find out nearest data points which are similar; then finally select actual centroids and formulate better clusters. According to the results of new solution, the improved k–means clustering algorithm provides more accuracy and effectiveness rather than previous one.
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