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A novel stratification clustering algorithm based on a new local density estimation method and an improved local inter-cluster distance measure

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第12期14卷 4251-4283页

作者： Qi, Jianfang Li, Yue Jin, Haibin Feng, Jianying Tian, Dong Mu, Weisong China Agr Univ Coll Informat & Elect Engn 53717 Qinghua East Rd Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

Recently clustering for datasets with different shapes, densities and noises has attracted more and more attention from scholars. However, most current clustering algorithms improve the clustering performance at the expense of the simplicity, and cannot balance well between the clustering quality and the operability for the users. To solve this problem, we propose a new algorithm called stratification clustering based on density, hierarchy and partition (SDHP) by effectively integrating the advantages of the density-based, hierarchical-based and partition-based clustering. First, a new parameter-free local density estimation strategy based on the bidirectional natural neighbor relationship named local density based on natural neighbor (NN-LD) is proposed to identify the core part of each sub-cluster. Then, a new stratification strategy based on the NN-LD Stratification-NN-LD (S-NN-LD) is proposed to divide the entire dataset into two layers, the core layer and the edge layer, to simplify the dataset structure and make the algorithm robust to noises. Next, the hierarchical-based single-linkage algorithm is adopted in the core layer to obtain the initial clustering result since it has advantages on clustering the datasets with various shapes and densities. Finally, to improve the clustering accuracy of samples in the edge layer, a combination of a new local inter-cluster distance measure based on the average of neighbor distances and the partitioning clustering is adopted to match these samples to the sub-clusters in the initial clustering result. The experiments on twenty datasets show that the SDHP has better clustering accuracy, and can be applied in practice well compared with four popular hierarchical clustering algorithms, four recent density-based clustering algorithms, and a state-of-the-art partitioning clustering algorithm. The source code can be downloaded from https:// ***/qi111678/SDHP.

关键词： Local density estimation single-linkage algorithm Inter-cluster distance measure Natural neighbor Stratification clustering

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A genetic approach to the automatic clustering problem

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PATTERN RECOGNITION 2001年第2期34卷 415-424页

作者： Tseng, LY Yang, SB Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

In solving the clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values of the parameter, w. A small value of w results in a larger number of compact clusters, while a large value of w results in a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to find a good clustering. Experimental results are given to illustrate the effectiveness of this genetic clustering algorithm. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： clustering single-linkage algorithm genetic clustering algorithm

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