Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm

In the era of big data, the research on clustering technologies is a popular topic because they can discover the structure of complex data sets with minimal prior knowledge. Among the existing soft clustering technologies, as an extension of fuzzy c-means (FCM) algorithm, the intuitionistic FCM (IFC...

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Vydáno v:International journal of machine learning and cybernetics Ročník 12; číslo 3; s. 859 - 875
Hlavní autoři: Hou, Wen-hui, Wang, Yi-ting, Wang, Jian-qiang, Cheng, Peng-Fei, Li, Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Témata:
Accuracy
Artificial Intelligence
Big Data
Cluster analysis
Clustering
Complex Systems
Computational Intelligence
Control
Datasets
Engineering
Fuzzy sets
Genetic algorithms
Heuristic
Mechatronics
Optimization algorithms
Original Article
Outliers (statistics)
Parameter identification
Pattern Recognition
Proximity
Robotics
Similarity measures
Systems Biology
Correlation coefficient
Genetic algorithm (GA)
Intuitionistic fuzzy c-means (IFCM) algorithm
Similarity measure
Intuitionistic fuzzification
ISSN:1868-8071, 1868-808X
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Shrnutí:In the era of big data, the research on clustering technologies is a popular topic because they can discover the structure of complex data sets with minimal prior knowledge. Among the existing soft clustering technologies, as an extension of fuzzy c-means (FCM) algorithm, the intuitionistic FCM (IFCM) algorithm has been widely used due to its superiority in reducing the effects of outliers/noise and improving the clustering accuracy. In the existing IFCM algorithm, the measurement of proximity degree between a pair of objects and the determination of parameters are two critical problems, which have considerable effects on the clustering results. Therefore, we propose an improved IFCM clustering technique in this paper. Firstly, a novel weighted proximity measure, which aggregates weighted similarity and correlation measures, is proposed to evaluate not only the closeness degree but also the linear relationship between two objects. Subsequently, genetic algorithms are utilized for identifying the optimal parameters. Lastly, experiments on the proposed IFCM technique are conducted on synthetic and UCI data sets. Comparisons with other approaches in cluster evaluation indexes indicate the effectiveness and superiority of our method.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01206-3