In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Grouping genetic algorithms: an efficient method to solve the cell formation problem
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
On Clustering Validation Techniques
Journal of Intelligent Information Systems
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CPGEA: a grouping genetic algorithm for material cutting plan generation
Computers and Industrial Engineering
Modular product design with grouping genetic algorithm: a case study
Computers and Industrial Engineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence Review
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A hybrid grouping genetic algorithm for the registration area planning problem
Computer Communications
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups
Expert Systems with Applications: An International Journal
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
A grouping genetic algorithm for the microcell sectorization problem
Engineering Applications of Artificial Intelligence
Evaluating performance advantages of grouping genetic algorithms
Engineering Applications of Artificial Intelligence
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Knowledge-Based Systems
Clustering of time series data-a survey
Pattern Recognition
An artificial bee colony approach for clustering
Expert Systems with Applications: An International Journal
A quantum-inspired genetic algorithm for k-means clustering
Expert Systems with Applications: An International Journal
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm
Expert Systems with Applications: An International Journal
Review: A particle swarm optimization approach to clustering
Expert Systems with Applications: An International Journal
A genetic clustering algorithm using a message-based similarity measure
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
ICE - Intelligent Clustering Engine: A clustering gadget for Google Desktop
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm's performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology.