Title: A hybrid model of fuzzy logic to enhance data mining accuracy incorporating intra-concentration and inter-separability loss into neighbourhood component analysis

Authors: Hemangini Mohanty; Santilata Champati

Addresses: Centre for Data Science, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar – 751030, Odisha, India ' Department of Mathematics, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar – 751030, Odisha, India

Abstract: Data mining is crucial to discovering meaningful insights and patterns from massive datasets. However, the accuracy and efficiency of data mining algorithms are often challenged by the curse of dimensionality and the complexity of real-world data. In this article, we propose a novel approach to enhance the accuracy of data mining by enriching the concept of intra-concentration and inter-separability (I2CS) loss into neighbourhood component analysis (NCA). NCA is a dimensionality reduction technique that focuses on preserving local neighbourhood information, thus improving classification accuracy. Fuzzy logic, on the other hand, provides a flexible framework to handle uncertainty and vagueness in data, enabling more nuanced decision-making. By integrating fuzzy C-means clustering with I2CS-NCA, we aim to leverage the complementary strengths of both approaches to enhance the accuracy and robustness of data mining algorithms. Also, the experimental results show that the proposed model gives the highest accuracy.

Keywords: I2CS loss; neighbourhood component analysis; NCA; fuzzy C-means clustering; random forest.

DOI: 10.1504/IJRIS.2026.152161

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.2, pp.65 - 71

Received: 17 Apr 2024
Accepted: 01 Jul 2024
Published online: 10 Mar 2026 *

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