A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain

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...

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Vydané v:Engineering applications of artificial intelligence Ročník 72; s. 213 - 227
Hlavní autori: Lu, Jianyun, Zhu, Qingsheng, Wu, Quanwang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.06.2018
Predmet:
[formula omitted]-nearest neighbors chain
Clustering algorithm
Data mining
Heuristic rule
Random walk model
K-nearest neighbors chain
Random walk model
Heuristic rule
Data mining
Clustering algorithm
ISSN:0952-1976, 1873-6769
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Popis
Shrnutí: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2018.03.014