A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain. (June 2018)

• Record Type: Journal Article
• Authors: Lu, Jianyun; Zhu, Qingsheng; Wu, Quanwang
• Is Part Of: Engineering applications of artificial intelligence. Volume 72(2018)
• Page Start: 213
• Page End: 227
• Publication Date: 2018-06
• Subjects: Clustering algorithm -- Random walk model -- K-nearest neighbors chain -- Heuristic rule -- Data mining; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.03.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In practice, clustering algorithms usually suffer from the complex structure of the dataset, including data distribution and dimensionality. Meanwhile, the number of clusters, which is required as an input, is usually unavailable. In this paper, we propose a novel data clustering algorithm: it uses heuristic rules based on k -nearest neighbors chain and does not require the number of clusters as the input parameter. Inspired by the PageRank algorithm, we first use random walk model to measure the importance of data points. Then, on the basis of the important data points, we build a K-Nearest Neighbors Chain (KNNC) to order the k nearest neighbors by distance and propose two heuristic rules to find the proper number of clusters and initial clusters. The first heuristic rule is the gap of KNNC which reflects the degree of separation of clusters with convex shapes and the second one is the nearest neighbor gap of KNNC which reflects the inner compactness of a cluster. Comprehensive comparison results on synthetic and real datasets indicate that the proposed clustering algorithm can find the proper number of clusters and achieve comparable or even better performance than the popular clustering algorithms.