An adaptive fuzzy-based algorithm with informed mutation for semi-supervised clustering
Subject Areas : Journal of Computer & RoboticsAtiyeh Taghizabet 1 , Amineh Amini 2 * , Jafar Taha 3 , Javad Mohammadzadeh 4
1 - Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
2 - Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Computer and Electrical Engineering Department, Tabriz University, Tabriz, Iran
4 - a. Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Keywords: Clustering, Semi-supervised, Multi-objective, Adaptive, Swarm intelligence, Fuzzy control, Informed mutation,
Abstract :
A technique that blends semi-supervised learning and clustering is called semi-supervised clustering. In semi-supervised clustering, using labeled data can significantly enrich the results quality. However, clustering is an NP-hard and multi-objective problem that necessitates the utilization of multi-objective meta-heuristic algorithms to generate satisfactory solutions. The main challenge associated with these algorithms lies in their susceptibility to local optima and the manual adjustment of parameters. To increase the diversity of non-dominated solutions, this study introduces an adaptive multi-objective cuckoo algorithm for semi-supervised clustering termed "Informed AdamCo," which has a mutation capacity based on preference information. The proposed method incorporates fuzzy control to modify the migration coefficient parameter and expands the single-objective cuckoo algorithm to its multi-objective counterpart. This adaptive-fuzzy adjustment of the parameter considers three practical inputs simultaneously: Iteration, Error, and Distance. Additionally, two coefficients have altered the cuckoo's movement pattern to improve the convergence of the multi-objective cuckoo method that has been given. To evaluate the proposed approach, 10 UCI and six synthetic datasets, as well as the KDD Cup 1999 dataset were used in the experiments. Statistical and numerical analyses demonstrate the superiority of Informed AdamCo over the other five algorithms compared.
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