Interval Kernel Fuzzy C-Means - Particle Swarm Optimizer with Two Differential Mutations (IKFCM-PSOTD) for Incomplete Data Clustering

DOI

10.2991/978-94-6463-413-6_18How to use a DOI?

Keywords

Incomplete data clustering; Interval imputations; Kernel based fuzzy c-means; Particle swarm optimization; Differential evolution

Abstract

Data processing and optimization are two major challenges in data analysis such as clustering. In practice, data often contains missing values that must be handled appropriately. This research provides an innovative approach to clustering incomplete data using Interval Kernel Fuzzy C-Means (IKFCM) technique optimized by Particle Swarm Optimizer with Two Differential Mutations (PSOTD). The proposed method solves the problem of incomplete data clustering by introducing interval imputation that allows more flexible handling of missing values. The interval value is obtained using the nearest neighbor method, which provides information on the similarity between an incomplete datum and its neighbors. Then, Kernel Fuzzy C-Means (KFCM) is applied due to its efficacy in handling outlier data and improving the accuracy of data representation in a high-dimensional feature space. In addition, Particle Swarm Optimization (PSO) algorithm adopting Differential Evolution (DE) technique with two different mutations is used to optimize the clustering algorithm to obtain better results. The addition of DE technique to PSO is believed to enhance global search capability and search efficiency. The proposed method is evaluated using the Partition Coefficient Index (PCI), Partition Entropy Index (PEI), and Apparent Error Rate (APER). Experimental results show that the proposed approach can efficiently cope with data incompleteness, resulting in more accurate clustering results than the comparison algorithms. In addition, the PSO algorithm enhanced with differential mutation makes the clustering result achieve a better solution.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Muhaimin Ilyas
AU  - Syaiful Anam
AU  - Trisilowati Trisilowati
PY  - 2024
DA  - 2024/05/13
TI  - Interval Kernel Fuzzy C-Means - Particle Swarm Optimizer with Two Differential Mutations (IKFCM-PSOTD) for Incomplete Data Clustering
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 182
EP  - 194
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_18
DO  - 10.2991/978-94-6463-413-6_18
ID  - Ilyas2024
ER  -