Clustering with Swarm Algorithms Compared to Emergent SOM

Herrmann, Lutz; Ultsch, Alfred

doi:10.1007/978-3-642-02397-2_10

Cited by 6 publications

(1 citation statement)

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“…Finally, agglomerate clustering algorithm was integrated into the iteration procedure of ant colony clustering algorithm. Herrmann and Ultsch (2009) presented a unifying representation for ant colony clustering (ACC) methods and emergent self-organizing maps (ESOM). The proposed unification allows to judge whether modifications improve an algorithm's clustering abilities or not, with a demonstration.…”

Section: Hybrid Evolutionary Algorithms For Clusteringmentioning

confidence: 99%

A survey: hybrid evolutionary algorithms for cluster analysis

Hasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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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.

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Section: Hybrid Evolutionary Algorithms For Clusteringmentioning

confidence: 99%