A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost
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School of Creative Convergence, Andong National University, Andong 36729, Korea
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School of Computer Engineering, Youngsan University, 288 Junam-Ro, Yangsan, Gyeongnam 50510, Korea
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Department of Data Informatics, (National) Korea Maritime and Ocean University, Busan 49112, Korea
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Authors to whom correspondence should be addressed.
Entropy 2020, 22(8), 902; https://doi.org/10.3390/e22080902
Received: 18 June 2020
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Revised: 27 July 2020
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Accepted: 11 August 2020
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Published: 17 August 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.
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Keywords:
initial seed; K-means; outliers; location division; density data; python data analysis; data science; hybrid; data driven
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MDPI and ACS Style
Jung, S.-H.; Lee, H.; Huh, J.-H. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy 2020, 22, 902. https://doi.org/10.3390/e22080902
AMA Style
Jung S-H, Lee H, Huh J-H. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy. 2020; 22(8):902. https://doi.org/10.3390/e22080902
Chicago/Turabian StyleJung, Se-Hoon, Hansung Lee, and Jun-Ho Huh. 2020. "A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost" Entropy 22, no. 8: 902. https://doi.org/10.3390/e22080902
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