An improved fuzzy c -means clustering algorithm based on shadowed sets and PSO
Jian Zhang,Ling Shen +1 more
TLDR
A modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering and significantly improves the clustering effect.Abstract:
To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters Experiments show that the proposed approach significantly improves the clustering effectread more
Citations
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Journal ArticleDOI
An improved multi-objective optimization model for supporting reservoir operation of China's South-to-North Water Diversion Project.
TL;DR: The modeling results indicated that the capacity of water diversion and storage for Danjiangkou Reservoir would be enhanced due to the operation of the South-to-North Water Diversion Project and the risks associated with possible flooding would be comparatively low under those four runoff guarantee rates.
Journal ArticleDOI
A general model of decision-theoretic three-way approximations of fuzzy sets based on a heuristic algorithm
TL;DR: Based on Deng’s model, the concept of a general three-way approximation of a fuzzy set is proposed to replace 0.5 with a variable value c(0.5) based on the principle of the minimum decision cost.
Journal ArticleDOI
A new data clustering algorithm based on critical distance methodology
TL;DR: The results prove that the new algorithm outperforms some popular clustering algorithms such as MST-based clustering, K-means, and Dbscan and can precisely produce more reasonable clusters even when the dataset contains outliers and without specifying any parameters in advance.
Journal ArticleDOI
Mean-entropy-based shadowed sets: A novel three-way approximation of fuzzy sets
TL;DR: In this study, a novel shadowed set model is proposed, namely, mean-entropy-based shadowed sets (MESS), a novel framework of three-way approximations of fuzzy sets is proposed based on the mean of fuzzy entropy.
Journal ArticleDOI
Log-Linear model based behavior selection method for artificial fish swarm algorithm
Zhehuang Huang,Yidong Chen +1 more
TL;DR: A new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection is proposed and implemented and has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
References
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Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Journal ArticleDOI
A Cluster Separation Measure
TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.
Journal ArticleDOI
Data clustering: 50 years beyond K-means
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
Book ChapterDOI
Data Clustering: 50 Years Beyond K-means
TL;DR: Cluster analysis as mentioned in this paper is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics, which is one of the most fundamental modes of understanding and learning.
Journal ArticleDOI
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
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