Abstract
Clustering is a technique that segregates a provided dataset into homogenous groups in accordance with the provided features. It aims to determine a structure in a group of unlabelled data. Cluster analysis is an unsupervised learning technology that determines the interesting patterns in data objects without class labels. K mode clustering algorithm seems to be effective in clustering categorical data due to its easy implementation and capability to handle the massive amount of data. But because of its random selectivity of initial centroids, it gives the local optimum solution. The main contribution of the paper is to evaluate the performance of clustering on the various dataset with the proposed system. The proposed method utilizes a genetic-based Metaheuristic encircle algorithm to select enriched features and novel dynamic K modes clustering based on Dimensionality Reduced PSO for clustering process with better computational time. The encircling Prey concept has been incorporated to choose the fitness function and overcome the genetic algorithm limitations in feature selection. This paper integrated the k-modes algorithm with particle swarm optimization algorithm to obtain a global optimum solution and update the initial centroid. Several dataset utilized for the evaluation of the proposed work has been found to achieve low accuracy in the previous work. But the proposed approach’s effectiveness has been proved to be better by performing a comparative analysis with the state of art methods in terms of performance metrics such as F1 score, accuracy, NMI.
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References
Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466
Agbaje MB, Ezugwu AE, Els R (2019) Automatic data clustering using hybrid firefly particle swarm optimization algorithm. IEEE Access 7:184963–184984
Ahmadyfard A, Modares H (2008) Combining PSO and k-means to enhance data clustering. 2008 Int Symp Telecomm:688–691
Alguliyev RM, Aliguliyev RM, Sukhostat LV (2020) Efficient algorithm for big data clustering on single machine. CAAI Trans Intell Technol 5:9–14
Bai L, Liang J, Cao F (2020) A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters. Inform Fusion 61:36–47
Cao F, Huang JZ, Liang J, Zhao X, Meng Y, Feng K et al (2017) An algorithm for clustering categorical data with set-valued features. IEEE Trans Neural Networks Learning Syst 29:4593–4606
Castro GT, Zárate LE, Nobre CN, Freitas HC (2019) A fast parallel K-modes algorithm for clustering nucleotide sequences to predict translation initiation sites. J Comput Biol 26:442–456
Ding Y, Zhou K, Bi W (2020) Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer. Soft Comput 24:1–10
K. S. Dorman and R. Maitra, "An Efficient $ k $-modes Algorithm for Clustering Categorical Datasets," arXiv preprint arXiv:2006.03936, 2020.
Ghany KKA, AbdelAziz AM, Soliman THA, Sewisy AAE-M (2020) A hybrid modified step whale optimization algorithm with Tabu search for data clustering. Journal of King Saud University-Computer and Information Sciences
Gupta T, Panda SP (2018) A comparison of k-means clustering algorithm and clara clustering algorithm on iris dataset. Int J Eng Technol 7:4766–4768
He H, Tan Y (2017) Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering. Appl Soft Comput 55:238–252
Heil J, Häring V, Marschner B, Stumpe B (2019) Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: a case study with west African soils. Geoderma 337:11–21
Hou J, Zhang A (2019) Enhancing density peak clustering via density normalization. IEEE Trans Industrial Inform 16:2477–2485
Islam MZ, Estivill-Castro V, Rahman MA, Bossomaier T (2018) Combining K-means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering. Expert Syst Appl 91:402–417
Jadhav AN, Gomathi N (2018) WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alexandria Eng J 57:1569–1584
Kumari S, Singh B (2020) Optimization of the distance between swarms using soft computing. Wirel Pers Commun:1–9
Kuo R, Zheng Y, Nguyen TPQ (2021) Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering. Inf Sci 557:1–15
Kurniati R, Arsalan O, Ramadhana Y (2021) Initial centroid determination using genetic algorithm in data clustering. Generic 13:6–9
Lai W, Zhou M, Hu F, Bian K, Song Q (2019) A new DBSCAN parameters determination method based on improved MVO. IEEE Access 7:104085–104095
Lakshmi K, Visalakshi NK, Shanthi S, Parvathavarthini S (2017) Clustering categorical data using k-modes based on cuckoo search optimization algorithm. ICTACT J Soft Computing 8
Liu C, Wang X, Huang Y, Liu Y, Li R, Li Y, … Liu J (2020) A moving shape-based robust fuzzy K-modes clustering algorithm for electricity profiles. Electr Power Syst Res 187:106425
Luchi D, Rodrigues AL, Varejão FM (2019) Sampling approaches for applying DBSCAN to large datasets. Pattern Recogn Lett 117:90–96
Naouali S, Salem SB, Chtourou Z (2020) Uncertainty mode selection in categorical clustering using the rough set theory. Expert Syst Appl 158:113555
Narayana GS, Kolli K (2020) Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset. Multimed Tools Appl 80:1–19
Narayana GS, Vasumathi D (2016) Clustering for high dimensional categorical data based on text similarity. Proceed 2nd Int Conf Commun Inform Process:17–21
Narayana GS, Vasumathi D (2018) An attributes similarity-based K-medoids clustering technique in data mining. Arab J Sci Eng 43:3979–3992
Nock R, Nielsen F (2006) On weighting clustering. IEEE Trans Pattern Anal Mach Intell 28:1223–1235
Pal R, Yadav S, Karnwal R (2020) EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs. Complex Intell Syst 6:1–10
Panagiotakis C (2015) Point clustering via voting maximization. J Classif 32:212–240
Prasanna K, Kumar MSP, Narayana GS (2011) A novel benchmark K-means clustering on continuous data. Int J Comp Sci Eng (IJCSE) 3:2974–2977
Rahnema N, Gharehchopogh FS (2020) An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimed Tools Appl 79:32169–32194
Sajidha S, Chodnekar SP, Desikan K (2018) Initial seed selection for K-modes clustering–a distance and density based approach. J King Saud Univ-Comp Inform Sci
Sangaiah AK, Fakhry AE, Abdel-Basset M, El-henawy I (2019) Arabic text clustering using improved clustering algorithms with dimensionality reduction. Clust Comput 22:4535–4549
Sekaran R, Goddumarri SN, Kallam S, Ramachandran M, Patan R, Gupta D (2021) 5G integrated Spectrum selection and Spectrum access using AI-based frame work for IoT based sensor networks. Comput Netw 186:107649
Sinaga KP, Yang M-S (2020) Unsupervised K-means clustering algorithm. IEEE Access 8:80716–80727
Singh T (2021) A novel data clustering approach based on whale optimization algorithm. Expert Syst 38:e12657
Wang Q, Liu R, Chen M, Li X (2021) Robust rank-constrained sparse learning: a graph-based framework for single view and Multiview clustering. IEEE Trans Cybernetics
H. Wilde, V. Knight, and J. Gillard (2020) A novel initialisation based on hospital-resident assignment for the k-modes algorithm," arXiv preprint arXiv:2002.02701 .
Yuan F, Yang Y, Yuan T (2020) A dissimilarity measure for mixed nominal and ordinal attribute data in k-modes algorithm. Appl Intell 50:1498–1509
Zhao Y-P, Chen L, Chen CP (2020) Laplacian regularized nonnegative representation for clustering and dimensionality reduction. IEEE Trans Circ Syst Video Technol 31:1–14
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Suryanarayana, G., Prakash K, L., Mahesh, P.C.S. et al. Novel dynamic k-modes clustering of categorical and non categorical dataset with optimized genetic algorithm based feature selection. Multimed Tools Appl 81, 24399–24418 (2022). https://doi.org/10.1007/s11042-022-12126-5
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DOI: https://doi.org/10.1007/s11042-022-12126-5