Handling Noise Data with PCA Method and Optimization Using Hybrid Fuzzy C-Means and Genetic Algorithm

Risa Widianti; Sugiyarto Surono; Kais Ismail Ibraheem

doi:10.30595/juita.v12i2.21765

Authors

Risa Widianti Ahmad Dahlan University
Sugiyarto Surono Ahmad Dahlan University
Kais Ismail Ibraheem Mosul University

DOI:

https://doi.org/10.30595/juita.v12i2.21765

Keywords:

noise data, Principal Component Analysis, Fuzzy C-Means, Genetic Algorithm, customer segmentation

Abstract

The significance of machine learning (ML) and data mining techniques particularly clustering is examined in this research, in managing large data sets for customer segmentation in the retail sector. The research emphasizes the challenges posed by data noise and proposes a solution using Principal Component Analysis (PCA) to improve accuracy. This study introduces a hybrid approach that combines Fuzzy C-Means (FCM) with genetic algorithms for optimization in customer segmentation, and suggests further research on the optimal number of clusters and data noise elimination. By addressing data noise, the proposed PCA-based method achieved a higher accuracy rate of 98% compared to 93% without PCA. This finding underscores the effectiveness of PCA in noise reduction, improving clustering accuracy. This research contributes to the advancement of customer-focused business strategies through better data analysis and interpretation. The proposed approach has potential applications in areas including data analysis, pattern recognition, and image processing, highlighting its relevance in the contemporary business environment.

Author Biographies

Risa Widianti, Ahmad Dahlan University

Mathematics Departement

Sugiyarto Surono, Ahmad Dahlan University

Mathematics Departement

Kais Ismail Ibraheem, Mosul University

Computers Science of Department

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