一种新的基于抑制的可能性模糊c均值聚类算法

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
J. Arora, M. Tushir, Shivank Kumar Dadhwal
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引用次数: 3

摘要

可能性模糊c-均值(PFCM)是目前应用最广泛的聚类算法之一,它解决了模糊c-均值(FCM)的噪声敏感性问题和可能性c-均值(PCM)的重合聚类问题。虽然PFCM是一种高可靠的聚类算法,但通过引入抑制的概念可以进一步提高算法的效率。基于抑制的算法在数据集上采用基于赢家和非赢家的抑制技术,有助于将现实世界的数据集更好地分类成簇。本文提出了一种基于抑制的可能性模糊c均值聚类算法(SPFCM)。本文探讨了基于各种真实数据集和合成数据集的错误分类数量的所提出方法的性能,并发现它在后续中比其他聚类技术(即基于正常和基于抑制的算法)表现更好。与其他聚类技术相比,SPFCM的执行效率更高,收敛速度更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm
Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly reliable clustering algorithm but  the efficiency of the algorithm can be further improved by introducing the concept of suppression. Suppression-based algorithms employ the winner and non-winner based suppression technique on the datasets, helping in performing better classification of real-world datasets into clusters. In this paper, we propose a suppression-based possibilistic fuzzy c-means clustering algorithm (SPFCM) for the process of clustering. The paper explores the performance of the proposed methodology based on number of misclassifications for various real datasets and synthetic datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as suppression-based algorithms. The SPFCM is found to perform more efficiently and converges faster as compared to other clustering techniques.
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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