Effective Density-Based Clustering Algorithms for Incomplete Data

作     者：Zhonghao Xue Hongzhi Wang 

作者机构：USC Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesCA 90007USA Department of Computer Science and TechnologyHarbin Institute of TechnologyHarbin 150001China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2021年第4卷第3期

页      面：183-194页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Nos.U1866602 and 71773025) the National Key Research and Development Program of China(No.2020YFB1006104) 

主　　题：density-based clustering incomplete data clustering algorihtm 

摘      要：Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for density-based clustering and decrease clustering result quality. To avoid these problems,we develop a novel density-based clustering approach for incomplete data based on Bayesian theory, which conducts imputation and clustering concurrently and makes use of intermediate clustering results. To avoid the impact of low-density areas inside non-convex clusters, we introduce a local imputation clustering algorithm, which aims to impute points to high-density local areas. The performances of the proposed algorithms are evaluated using ten synthetic datasets and five real-world datasets with induced missing values. The experimental results show the effectiveness of the proposed algorithms.