Cluster ensemble of valid small clusters

• Published in: Journal of intelligent & fuzzy systems Vol. 39; no. 1; pp. 525 - 542
• Main Authors: Li, Guang, Mahmoudi, Mohammad Reza, Qasem, Sultan Noman, Tuan, Bui Anh, Pho, Kim-Hung
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2020; Sage Publications Ltd
• Subjects: Algorithms; Cluster analysis; Clustering; Data mining; Partitions; Vector quantization; small cluster; cluster ensemble; kmeans clustering; Graph representation
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-191530

During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that “the model combining several poorer models is better than a stronger model”. Here at this paper, an ensemble clustering method is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available.