Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters

2006 ◽  
Author(s):  
Heng Zhao ◽  
Jimin Liang ◽  
Haihong Hu
Keyword(s):  
Gamma Statistic ◽  
Clustering Validity

Clustering validity measure based on intuitionistic fuzzy inclusion degree

2010 ◽  
Vol 30 (6) ◽  
pp. 1527-1529
Author(s):  
Ge ZHANG ◽  
Ying-jie LEI ◽  
Xing-long ZHAI ◽  
Hong-jing ZHAO
Keyword(s):  
Inclusion Degree ◽  
Intuitionistic Fuzzy ◽  
Clustering Validity ◽  
Validity Measure

scholarly journals Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

2016 ◽  
Vol 16 (6) ◽  
pp. 27-42 ◽  
Author(s):  
Minghan Yang ◽  
Xuedong Gao ◽  
Ling Li
Keyword(s):  
Simulated Annealing ◽  
Data Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Parameter Determination ◽  
Data Sets ◽  
Parameter Vector ◽  
Clustering Validity

Abstract Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

On strategies for building effective ensembles of relative clustering validity criteria

2015 ◽  
Vol 47 (2) ◽  
pp. 329-354 ◽  
Author(s):  
Pablo A. Jaskowiak ◽  
Davoud Moulavi ◽  
Antonio C. S. Furtado ◽  
Ricardo J. G. B. Campello ◽  
Arthur Zimek ◽  
...  
Keyword(s):  
Validity Criteria ◽  
Clustering Validity

Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics

2010 ◽  
pp. 257-280
Author(s):  
Nicola Fanizzi ◽  
Claudia d’Amato ◽  
Floriana Esposito
Keyword(s):  
Distance Measure ◽  
Concept Drift ◽  
Fitness Function ◽  
Description Logics ◽  
Optimization Method ◽  
Optimal Choice ◽  
Validity Indices ◽  
Evolutionary Operators ◽  
Clustering Validity ◽  
The Given

We present a method based on clustering techniques to detect possible/probable novel concepts or concept drift in a Description Logics knowledge base. The method exploits a semi-distance measure defined for individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (concept descriptions). A maximally discriminating group of features is obtained with a randomized optimization method. In the algorithm, the possible clusterings are represented as medoids (w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter, the method is able to find an optimal choice by means of evolutionary operators and a proper fitness function. An experimentation proves the feasibility of our method and its effectiveness in terms of clustering validity indices. With a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.

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