Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The Journal of Machine Learning Research
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A Rough Set Theoretic Approach to Clustering
Fundamenta Informaticae
A New Clustering Algorithm Based on Token Ring
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Genetic algorithms applied to clustering problem and data mining
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
OWA-weighted based clustering method for classification problem
Expert Systems with Applications: An International Journal
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
Learning with Positive and Unlabeled Examples Using Topic-Sensitive PLSA
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Improving inference through conceptual clustering
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Study on collaborative filtering recommendation algorithm based on web user clustering
International Journal of Wireless and Mobile Computing
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
A multidimensional scaling localisation algorithm based on bacterial foraging optimisation
International Journal of Wireless and Mobile Computing
Video stabilisation using local salient feature in particle filter framework
International Journal of Wireless and Mobile Computing
A novel complex community network division algorithm with multi-gene families encoding
International Journal of Wireless and Mobile Computing
International Journal of Wireless and Mobile Computing
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Clustering analysis is primarily concerned with the classification of data points into different clusters. Estimation of distribution algorithms (EDAs) uses machine learning techniques to solve optimisation problems by trying to learn the locations of the more promising regions of the search space. In EDAs a population may be approximated with a probability distribution, and new candidate solutions can be obtained by sampling from this distribution, instead of combining and modifying existing solutions in a stochastic way. Unsupervised clustering learning algorithm based on estimation of distribution (EDA-USL) is designed to solve the analysis of dataset without labels. EDA-USL randomly selects a few data as individuals to construct initial population. The probability distribution of population is computed to estimate the distribution of dataset. The optimal individuals in population are selected by the designed fitness function. Then the new individuals that combine with the optimal ones to form the next generation are selected according to the classification patterns of the optimal individuals. EDA-USL is validated on the benchmark datasets and analysed. The experimental results show that EDA-USL has high stability and performs well in classification accuracy.