Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data

Abstract

Categorical data clustering has been attracted a lot of attention recently due to its necessary in the real-world applications. Many clustering methods have been proposed for categorical data. However, most of the existing algorithms require the predefined number of clusters which is usually unavailable in real-world problems. Only a few works focused on automatic clustering, but mainly handled for numerical data. This study develops a novel automatic fuzzy clustering using non-dominated sorting particle swarm optimization (AFC-NSPSO) algorithm for categorical data. The proposed AFC-NSPSO algorithm can automatically identify the optimal number of clusters and exploit the clustering result with the corresponding selected number of clusters. In addition, a new technique is investigated to identify the maximum number of clusters in a dataset based on the local density. To select a final solution in the first Pareto front, some internal validation indices are used. The performance of the proposed AFC-NSPSO on the real-world datasets collected from the UCI machine learning repository exhibits effectiveness compared with some other existing automatic categorical clustering algorithms. Besides, this study also applies the proposed algorithm to analyze a real-world case study with an unknown number of clusters.INDEX TERMS Automatic clustering, categorical data, local density, NSPSO.

A Similarity based K-Means Clustering Technique for Categorical Data in Data Mining Application

“…In this section, the validation of SKC method is analyzed against various existing techniques such as AFC-NSPSO [14], CrKMd [16] and other popular techniques like Support Vector Machine (SVM) and Naive Bayes (NB). The existing AFC-NSPSO and CrKMd conducted the experiments only on mushroom dataset.…”

“…For connect dataset, the existing techniques namely SVM, NB, AFC-NSPSO and CrKMd achieved only 76% to 79% of accuracy, where proposed SKC achieved 81.47% of accuracy. The existing AFC-NSPSO [14] and CrKMd [16] didn't consider the removal of outliers before clustering process, where SKC removed the outliers that leads high performance on accuracy. This is due to the distance measures used in the SKC method for clustering the data.…”

“…In this section, a discussion of various existing techniques is presented, which are used to cluster the categorical data. The advantages and its limitations of these existing techniques [14][15][16][17][18] are also illustrated.…”

“…Nguyen, and Kuo [14] clustered the categorical data by developing an Automatic Fuzzy Clustering with Non-Dominated Sorting PSO as AFC-NSPSO algorithm. The number of clusters and partition data instances were automatically determined by the developed study.…”

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

“…The authors suggested in future to grouping the massive biological datasets in the new technology called Big data. Nguyen and Kuo (2019) developed a novel automatic fuzzy clustering usingnon-dominated sorting particle swarm optimization (AFC-NSPSO) algorithm for categorical data. The proposed AFC-NSPSO algorithm can automatically identify the optimal number of clusters and exploit the clustering result with the corresponding selected number of clusters.…”