Abstract: A method of performing K-means clustering by a data mining system is provided. The method includes generating a plurality of initial buckets by dividing data including a plurality of points each being expressed in coordinate information, reflecting a count noise in a number of points included in each of the initial buckets and then generating a plurality of new buckets by dividing at least one initial bucket among the initial buckets based on a first threshold and a second threshold, generating a plurality of final buckets from the plurality of initial buckets or the plurality of new buckets, generating a histogram including section information for each of the final buckets and a number of points included in each of the final buckets in which the count noise is reflected, and performing K-means clustering on the histogram based on a number of clusters.

Abstract: A method of performing K-means clustering by a data mining system is provided. The method includes generating a plurality of initial buckets by dividing data including a plurality of points each being expressed in coordinate information, reflecting a count noise in a number of points included in each of the initial buckets and then generating a plurality of new buckets by dividing at least one initial bucket among the initial buckets based on a first threshold and a second threshold, generating a plurality of final buckets from the plurality of initial buckets or the plurality of new buckets, generating a histogram including section information for each of the final buckets and a number of points included in each of the final buckets in which the count noise is reflected, and performing K-means clustering on the histogram based on a number of clusters.