Genetic algorithm-based clustering technique

By Ujjwal Maulik, Sanghamitra Bandyopadhyay. Pattern Recognition, 2000

1,487 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6e483ec7530df88a1960b377a609d9d709ba7dd6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, positive): “[300] M.K. Pakhira, S. Bandyopadhyay, U. Maulik, Validity index for crisp and fuzzy clusters, Pattern Recognit.”
Performance Evaluation of Some Clustering Algorithms and Validity Indices (2002, methodology): “THE purpose of any clustering technique [1], [2], [3], [4], [5] is to evolve a K n partition matrix UðXÞ of a data set X (X 1⁄4 fx1; x2; .”
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis (2010, methodology): “Mualik and Bandyopadhyay have proposed a genetic algorithm based method to solve the clustering problem and experiment on synthetic and real life datasets to evaluate the performance [5].”
Particle swarm optimization method for image clustering (2005, background): “On the other hand, due to its ability to handle large search spaces with minimum information about the objective function, GAs have been used to develop GA-based clustering algorithms [13, 33].”
A Survey of Evolutionary Algorithms for Clustering (2009, methodology): “: Proportional selection [6] has been used by several authors (e.g., Krovi [84]; Lucasius et al. [96]; Murthy and Chowdhury [107]; Estivill-Castro and Murray [39]; Fränti et al. [48]; Maulik and Bandyopadhyay [100]; Kivijärvi et al. [79]; Bandyopadhyay and Maulik [10]; Krishna and Murty [83]; Lu et ”
Black hole: A new heuristic optimization approach for data clustering (2013, methodology): “For instance, simulated annealing [33], tabu search [58], genetic algorithms [57,62,63,66], ant colony optimization [28,67,89], neural gas algorithm [72–74], honey bee mating optimization [23], differential evolution algorithm [15,16], particle swarm optimization algorithm [1,44,54], artificial bee ”
A recommender system using GA K-means clustering in an online shopping market (2008, methodology): “Prior studies pointed out this limitation and tried to integrate K-means clustering and global search techniques including genetic algorithms (see Babu & Murty, 1993; Kuo, Liao, & Tu, 2005; Maulik & Bandyopadhyay, 2000; Murthy & Chowdhury, 1996; Pena, Lozano, & Larranaga, 1999).”
On digital soil mapping (2003, background): “Maulik and Bandyopadhyay ( 2000 ) proposed a genetic algorithm for unsuper - vised classification .”
Survey of clustering algorithms (2005, methodology): “After the maximum number of clusters and the candidate models are speci ﬁ ed, an agglomerative hierarchical clustering was used to ignite the EM algorithm by forming an initial partition, which includes at most the maximum number of clusters, for each model.”
Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles (2016, background): “Instead, several validity measure metrics are calculated to examine the goodness of the partitioning result, including partition coefficient (PC) [34], Xie and Beni’s Index (XB) [35], and PBMF [36], [37].”
Genetic algorithms: theory, genetic operators, solutions, and applications (2023, neutral): “Also, GAs are used to improve the clustering approaches by finding the optimal clusters’ centroids [60, 80, 81] and the optimal clusters (i.e., each chromosome as a set of clusters) [82, 83].”
Differential evolution and particle swarm optimisation in partitional clustering (2006, background): “Moreover, many authors proposed cluster centroids, baricentres, or medoids as representation points to allocate each object to a specific cluster (e.g. Maulik and Bandyopadhyay 2000, Chiou and Lan, 2001, Bandyopadhyay and Maulik 2002a, Paterlini and Minerva 2003).”