IKM-NCS: A Novel Clustering Scheme Based on Improved K-Means Algorithm
Author:
Wang Weipeng1, Tu Shanshan2, Huang Xinyi3
Affiliation:
1. eijing Electro-Mechanical Engineering Institute, 100074, Beijing, China 2. eijing Key Laboratory of Trusted Computing, Faculty of Information Technology, Beijing University of Technology, 100124, Beijing, China 3. Faculty of Information Technology, Beijing University of Technology, 100124, Beijing, China
Abstract
Aiming at the problems of distorted center selection and slow iteration convergence in traditional clustering analysis algorithm, a novel clustering scheme based on improved k-means algorithm is proposed. In this paper, based on the analysis of all user behavior sets contained in the initial sample, a weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior set are proposed and a set of abnormal behaviors is constructed for each user according to the behavior data generated by abnormal users. Then, on the basis of the traditional k-means clustering algorithm, an improved algorithm is proposed. By calculating the compactness of all data points and selecting the initial cluster center among the data points with high and low compactness, the clustering performance is enhanced. Finally, the eigenvalues of the abnormal behavior set are used as the input of the algorithm to output the clustering results of the abnormal behavior. Experimental results show that the clustering performance of this algorithm is better than the traditional clustering algorithm, and can effectively improve the clustering performance of abnormal behavior
Publisher
North Atlantic University Union (NAUN)
Subject
Applied Mathematics,Computational Mathematics,Mathematical Physics,Modeling and Simulation
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