Talk · ITM Web of Conferences · January 1, 2019

Clusterization by the K-means method when K is unknown

Natalya Litvinenko, Orken J. Mamyrbayev, Assem Shayakhmetova, Mussa Turdalyuly

Abstract

There are various methods of objects’ clusterization used in different areas of machine learning. Among the vast amount of clusterization methods, the K-means method is one of the most popular. Such a method has as pros as cons. Speaking about the advantages of this method, we can mention the rather high speed of objects clusterization. The main disadvantage is a necessity to know the number of clusters before the experiment. This paper describes the new way and the new method of clusterization, based on the K-means method. The method we suggest is also quite fast in terms of processing speed, however, it does not require the user to know in advance the exact number of clusters to be processed. The user only has to define the range within which the number of clusters is located. Besides, using suggested method there is a possibility to limit the radius of clusters, which would allow finding objects that express the criteria of one cluster in the most distinctive and accurate way, and it would also allow limiting the number of objects in each cluster within the certain range.

Advanced Clustering Algorithms ResearchData Mining and Machine Learning ApplicationsBayesian Methods and Mixture ModelsK-means clusteringunknown number of clustersoptimal number of clusters

Clusterization by the K-means method when K is unknown

Abstract

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