A Survey on Clustering Algorithms for Partitioning Method

Abstract

Clustering is one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances. Whatever a clustering algorithm provides a better performance, it has the more successful to achieve this goal. Nowadays, although many research done in the field of clustering algorithms, these algorithms have the challenges such as processing time, scalability, accuracy, etc. Comparing various methods of the clustering, the contributions of the recent researches focused on solving the clustering challenges of the partition method. In this paper, the partitioning clustering method is introduced, the procedure of the clustering algorithms is described, and finally the new improved methods and the proposed solutions to solve these challenges are explained.

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“…K-means is scalable but cannot use for flexible data. This is further evidenced as weakness in the fact that; agglomerative clustering methods do not scale well [174], also unfortunately with hierarchical clustering once a step (merge or split) is done, it can never be undone [175].…”

Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

“…The sensitivity to noisy and outlier data is one of FCM problems that a robust clustering approach called TCLUST [8] , fuzzy c-means-relaxed constraints support vector machine (FCM-RSVM) [9] , Relative entropy fuzzy c-means (REFCM) [10], and algorithms based on type-2 fuzzy sets such as [11,12] have recently been proposed to solve this problem. In addition, a comparison of partition algorithms is presented by Khanali and Vaziri in [13] . Sefidian and Daneshpour in [14] by combining grey system theory concepts and FCM to provide the clustering accuracy called grey based fuzzy c-means and mutual information (GFCMI) based feature selection imputation.…”

“…The descriptive methods explain the relationships between the data regardless of the label or external variable. Clustering [13], association rules [27], and sequential patterns are among the methods of learning a model with the nature of descriptive. The clustering method is a model learning method that has a descriptive nature.…”