Haifeng Yang , Weiqi Wang , Jianghui Cai , Jie Wang , Yating Li , Yaling Xun , Xujun Zhao
{"title":"Three-way clustering based on the graph of local density trend","authors":"Haifeng Yang , Weiqi Wang , Jianghui Cai , Jie Wang , Yating Li , Yaling Xun , Xujun Zhao","doi":"10.1016/j.ijar.2025.109422","DOIUrl":null,"url":null,"abstract":"<div><div>Three-way clustering demonstrates its unique advantages in dealing with the issues of information ambiguity and unclear boundaries present in real-world datasets. The core and boundary region in the data are identified as key features of cluster analysis. Typically, data is segmented into three regions based on a set of predetermined global thresholds, a common practice in three-way clustering. However, this method, which relies on global thresholds, often overlooks the intrinsic distribution patterns within the dataset and determining these thresholds a priori can be quite challenging. In this paper, we propose a three-way clustering method based on the graph of local density trend (3W-GLDT). Specifically, the algorithm first uses a density-decreasing strategy to build subgraphs and divide the core region data. Then, the unreasonable connection is corrected by using isolated forest, which increases the number of core points and enlarges the distribution range of core points. Next, a three-way allocation strategy is proposed, which fully considers the degree of local aggregation of subgraphs and the natural domain information of each data object to ensure the correct allocation. Finally, the proposed algorithm is compared with 8 different clustering methods on 8 synthetic datasets and 10 UCI real datasets. The experimental results show that the 3W-GLDT algorithm has good performance and clustering results.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"182 ","pages":"Article 109422"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X25000635","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Three-way clustering based on the graph of local density trend
Three-way clustering demonstrates its unique advantages in dealing with the issues of information ambiguity and unclear boundaries present in real-world datasets. The core and boundary region in the data are identified as key features of cluster analysis. Typically, data is segmented into three regions based on a set of predetermined global thresholds, a common practice in three-way clustering. However, this method, which relies on global thresholds, often overlooks the intrinsic distribution patterns within the dataset and determining these thresholds a priori can be quite challenging. In this paper, we propose a three-way clustering method based on the graph of local density trend (3W-GLDT). Specifically, the algorithm first uses a density-decreasing strategy to build subgraphs and divide the core region data. Then, the unreasonable connection is corrected by using isolated forest, which increases the number of core points and enlarges the distribution range of core points. Next, a three-way allocation strategy is proposed, which fully considers the degree of local aggregation of subgraphs and the natural domain information of each data object to ensure the correct allocation. Finally, the proposed algorithm is compared with 8 different clustering methods on 8 synthetic datasets and 10 UCI real datasets. The experimental results show that the 3W-GLDT algorithm has good performance and clustering results.
期刊介绍:
The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest.
Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning.
Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.