Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data

Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering me...

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Bibliographic Details
Published in:Neural computing & applications Vol. 34; no. 20; pp. 18059 - 18074
Main Authors: Nguyen, Thi Phuong Quyen, Kuo, R. J., Le, Minh Duc, Nguyen, Thi Cuc, Le, Thi Huynh Anh
Format: Journal Article
Language:English
Published: London Springer London 01.10.2022
Springer Nature B.V
Subjects:
Artificial Intelligence
Centroids
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Genetic algorithms
Image Processing and Computer Vision
Machine learning
Original Article
Probability and Statistics in Computer Science
Searching
Local search genetic algorithm
Possibilistic fuzzy
means
Variable neighborhood search
Mixed data
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering methods are sensitive to the initial solutions and easily trap into a locally optimal solution. Thus, this study proposes a local search genetic algorithm-based possibilistic weighted fuzzy c -means (LSGA-PWFCM) for clustering mixed numerical and categorical data. The possibilistic weighted fuzzy c-means (PWFCM) is firstly proposed in which the object-cluster similarity measure is employed to calculate the distance between two mixed-attribute objects. Besides, each attribute is placed a different important role by calculating its corresponding weight in the PWFCM procedure. Thereafter, GA is used to find a set of optimal parameters and the initial clustering centroids for the PFCM algorithm. To avoid local optimal solution, local search-based variable neighborhoods are embedded in the GA procedure. The proposed LSGA-PWFCM algorithm is compared with other benchmark algorithms based on some public datasets in UCI machine learning repository to evaluate its performance. Two clustering validation indices are used, i.e., clustering accuracy and Rand index. The experimental results show that the proposed LSGA-PWFCM outperforms other algorithms on most of the tested datasets.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07411-1