Journal ArticleDOI
A survey: hybrid evolutionary algorithms for cluster analysis
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This paper provides a survey of hybrid evolutionary algorithms for cluster analysis using both ant-based and swarm-based algorithms as an alternative to more traditional clustering techniques.Abstract:
Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.read more
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Journal ArticleDOI
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data
TL;DR: This paper presents a literature survey on the PSO algorithm and its variants to clustering high-dimensional data and an attempt is made to provide a guide for the researchers who are working in the area of PSO and high- dimensional data clustering.
[IEEE IEEE International Conference on Networking, Sensing and Control, 2004 - Taipei, Taiwan (March 21-23, 2004)] IEEE International Conference on Networking, Sensing and Control, 2004 - Particle swarm optimization algorithm and its application to clustering analysis
Ching-Yi Chen,Fun Ye +1 more
TL;DR: In this article, a particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed to search the cluster center in the arbitrary data set automatically, which can help the user to distinguish the structure of data and simplify the complexity of data from mass information.
Journal ArticleDOI
Dynamic clustering with improved binary artificial bee colony algorithm
TL;DR: The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers.
An ACO-Based Clustering Algorithm
Yucheng Kao,Kevin Cheng +1 more
TL;DR: Ant Colony Optimization for Clustering (ACOCO) as mentioned in this paper uses both accumulated pheromone and the heuristic information, the distances between data objects and cluster centers of ants, to guide artificial ants to group data objects into proper clusters.
Journal ArticleDOI
Evolutionary Machine Learning: A Survey
TL;DR: In this article, the role of evolutionary machine learning (EC) algorithms in solving different ML challenges has been investigated, including feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.