A Hybrid Evolutionary Approach to Cluster Detection

  • Junping Sun
  • William Sverdlik
  • Samir Tout
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

The modern world has witnessed a surge in technological advancements that span various industries. In some sectors, such as search engines, bioinformatics, and pattern recognition, software applications typically deal with having to interpret shear amounts of data in an attempt to discover patterns that may provide great value for business analysis, development, and planning. This emphasized the importance of fields of study such as clustering, a descendant discipline of data mining, which gained momentum in recent decades. Clustering addresses this very problem of analyzing large datasets and attempting to unravel data distributions and patterns by means of a mostly unsupervised data classification [9]. Example clustering applications include multimedia analysis and retrieval [10], pattern recognition [15], and bioinformatics [5].

This chapter starts by providing an overview of existing clustering approaches. Then, it defines key concepts that are utilized by the PYRAMID algorithm. It also presents the experiments that were conducted in Tout et al. [23] as well as other experiments using various datasets that were employed in Sheikholeslami et al. [21] featuring different challenges. Finally, it explores the independence of PYRAMID on user-supplied parameters and outlines future research directions.


Genetic Programming Cluster Detection Data Parallelism Slave Processor Master Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Junping Sun
    • 1
  • William Sverdlik
    • 2
  • Samir Tout
    • 2
  1. 1.Graduate School of Computer and Information SciencesNova Southeastern UniversityFort LauderdaleUSA
  2. 2.Department of Computer ScienceEastern Michigan UniversityYpsilantiUSA

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