Fuzzy clustering

About: Fuzzy clustering is a research topic. Over the lifetime, 23230 publications have been published within this topic receiving 601269 citations.

Hierarchical fuzzy clustering based on self-organising networks

Abstract: A fast and computationally efficient fuzzy clustering approach is presented. In this approach, fuzzy clustering is implemented in two hierarchical phases: subclusters generation by a self-organising network and fuzzy classification via a fuzzy competitive clustering network associated with a fuzzy c-means algorithm. Owing to the hierarchical network, the computation complexity of fuzzy clustering is reduced drastically and the clustering performance is enhanced as well. The simulation results show that the proposed method has a much higher computing efficiency and better classification performance compared to standard fuzzy c-means clustering.

Improved Fuzzy Clustering Method Based on Entropy Coefficient and Its Application

Abstract: Based on the principle of fuzzy clustering analysis and the theory of entropy, an improved fuzzy clustering method is given by improving the method of establishing the membership function, combining the clustering weight with the entropy coefficient, and replacing the Zadeh operator M($\bigvee,\bigwedge$) with the weight average operator M(±, i¾?). With the improved method, the zeroweight problem is addressed effectively, the weights of each factor are modified properly and the phenomenon of Major Factor Dominating is also alleviated appropriately. Finally, an illustrative example is given to clarify the method, which shows that the improved fuzzy clustering method is reasonable, feasible, simple and practical.

A novel approach for distance-based semi-supervised clustering using functional link neural network

Abstract: Semi-supervised clustering is gaining importance these days since neither supervised nor unsupervised learning methods in a stand-alone manner provide satisfactory results. Existing semi-supervised clustering techniques are mostly based on pair-wise constraints, which could be misleading. These semi-supervised clustering algorithms also fail to address the problem of dealing with attributes having different weights. In most of the real-life applications, all attributes do not have equal importance and hence same weights cannot be assigned for each attribute. In this paper, a novel distance-based semi-supervised clustering algorithm has been proposed, which uses functional link neural network (FLNN) for finding weights for attributes with small amount of labeled data for further use in parametric Minkowski's model for clustering. In FLNN, the nonlinearity is captured by enhancing the input using orthonormal basis functions. The effectiveness of the approach has been illustrated over a number of datasets taken from UCI machine learning repository. Comparative performance evaluation demonstrates that the proposed approach outperforms the existing semi-supervised clustering algorithms. The proposed approach has also been successfully used to cluster the crime locations and to find crime hot spots in India on the data provided by National Crime Records Bureau (NCRB).

An Improved Genetic Algorithm for Spatial Clustering

Abstract: This paper proposes a real-coded genetic algorithm (GA) with a new flexible gene structure for spatial clustering problems. The basic idea is to improve the solution quality and rate of cluster detection by employing flexible ellipses moving and shifting in all directions. Based on synthetic and real datasets, a performance test is conducted to evaluate the quality of the improvements in the proposed genetic algorithm. The result indicates configuration of the new gene structure and solution representation allows for full exploration of the solution spaces as well as provides better solution quality and cluster detection rates.

Expressing Relevance Interpretability and Accuracy of Rule-Based Systems

Abstract: We discuss a problem of synthesis and analysis of rules based on experimental numeric data and study their interpretation capabilities. Two descriptors of the rules being viewed individually and en block are introduced. The relevance of the rules is quantified in terms of the data being covered by the antecedents and conclusions standing in the rule. While this index describes each rule individually, the consistency of the rule deals with the quality of the rule viewed vis-a-vis other rules. It expresses how much the rule “interacts ” with others in the sense that its conclusion is distorted by the conclusion parts coming from other rules. We show how the rules are formed by means of fuzzy clustering and their quality evaluated by means of the above indexes. We also discuss a construction of a granular mapping (that is a mapping between fuzzy clusters in the input and output spaces) and quantify its performance (approximation capabilities at the numeric level). Global characteristics of a set of rules are also discussed and related to the number of information granules formed in the space of antecedents and conclusions.