MATEC Web Conf.
Volume 176, 20182018 6th International Forum on Industrial Design (IFID 2018)
|Number of page(s)||5|
|Section||Intelligent Design and Computer Technology|
|Published online||02 July 2018|
An Improved K-means Method with Density Distribution Analysis
College of Science, China Agricultural University, Tsinghua East Road, Beijing, China
In this paper, a novel K-means clustering algorithm is proposed. Before running the traditional Kmeans, the cluster centers should be randomly selected, which would influence the time cost and accuracy. To solve this problem, we utilize density distribution analysis in the traditional K-means. For a reasonable cluster, it should have a dense inside structure which means the points in the same cluster should tightly surround the center, while separated away from other cluster canters. Based on this assumption, two quantities are firstly introduced: the local density of cluster center ρi and its desperation degree δi, then some reasonable cluster centers candidates are selected from the original data. We performed our algorithm on three synthetic data and a real bank business data to evaluate its accuracy and efficiency. Comparing with Traditional K-means and K-means++, the results demonstrated that the improved method performs better.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.