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Evolutionary algorithms for data stream clustering

Grant number: 12/10396-6
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): September 01, 2012
Effective date (End): February 28, 2013
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Jonathan de Andrade Silva
Supervisor: João Manuel Portela da Gama
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: Universidade do Porto (UP), Portugal  
Associated to the scholarship:10/15049-7 - Clustering Data Streams with Automatic Estimation of Number of Clusters, BP.DR


Data clustering techniques are commonly used to find clusters. The number of clusters is usually a priori unknown. Such techniques assume that the data set is fixed-size and can be stored entirely into main memory. However, an actual and important challenge involves applying such data clustering techniques into sources of data in which data flows continuously over dynamic environments. These data sources are known as data streams. In many data stream applications, data access operations are restricted to one (or to a small number) of passes over the data with time and memory restrictions. In this sense, some data stream clustering algorithms have been proposed in literature. Many of these techniques are based on the k-means algorithm. However, k-means suffers from several major drawbacks, particularly related to local minima and to the need of specifying the number of clusters in advance. In this context, the main goal of this project involves the development and evaluation of evolutionary algorithms for data stream clustering that estimate automatically the number of clusters from data. (AU)

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