Data mining clustering of high dimensional databases with evolutionary algorithms

  title={Data mining clustering of high dimensional databases with evolutionary algorithms},
  author={Ioannis Sarafis},
Driven by advances in data collection and storage, increasingly large and high dimensional datasets are being stored. Without special tools, human analysts can no longer make sense of such enormous volumes of data. Hence, intelligent data mining (DM) techniques are being developed to semi-automate the process of mining nuggets of hidden knowledge, and extract them in forms that can be readily utilised in areas such as decision support. Clustering high dimensional data is especially challenging… CONTINUE READING


Publications citing this paper.
Showing 1-4 of 4 extracted citations

A Survey of Evolutionary Algorithms for Clustering

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) • 2009
View 5 Excerpts
Highly Influenced

A Review of Evolutionary Algorithms for Data Mining

Data Mining and Knowledge Discovery Handbook • 2010
View 5 Excerpts
Highly Influenced

Efficient technique for personalized web search using users browsing history

International Conference on Computing, Communication & Automation • 2015
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 91 references

Data Mining : Concepts and Techniques

View 20 Excerpts
Highly Influenced

H-P Kriegel

M. Ester
J.Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge Discovery and Data Mining, pages 226–231. AAAI Press • 1996
View 16 Excerpts
Highly Influenced

Active crustal deformation from the azores triple junction to middle east

A. Kiratzi, C. B. Papazachos
Tectonophysics, 000000(243):1–24 • 1995
View 20 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…