Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
ACM Computing Surveys (CSUR)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
SiMCAL 1 algorithm for analysis of gene expression data related to the phosphatidylserine receptor
Artificial Intelligence in Medicine
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
On comparing two sequences of numbers and its applications to clustering analysis
Information Sciences: an International Journal
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Engineering Applications of Artificial Intelligence
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
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We introduce a novel clustering algorithm named GAKREM (Genetic Algorithm K-means Logarithmic Regression Expectation Maximization) that combines the best characteristics of the K-means and EM algorithms but avoids their weaknesses such as the need to specify a priori the number of clusters, termination in local optima, and lengthy computations. To achieve these goals, genetic algorithms for estimating parameters and initializing starting points for the EM are used first. Second, the log-likelihood of each configuration of parameters and the number of clusters resulting from the EM is used as the fitness value for each chromosome in the population. The novelty of GAKREM is that in each evolving generation it efficiently approximates the log-likelihood for each chromosome using logarithmic regression instead of running the conventional EM algorithm until its convergence. Another novelty is the use of K-means to initially assign data points to clusters. The algorithm is evaluated by comparing its performance with the conventional EM algorithm, the K-means algorithm, and the likelihood cross-validation technique on several datasets.