Development of Clustering Techniques with Nature Inspired Algorithms and CUDA-based Implementation

• Grant number: 13/08730-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2013
• End date: June 30, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Adriane Beatriz de Souza Serapião
• Grantee: Felipe Bonon Gonçalves
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil

Abstract

Cluster analysis is one of the main techniques in the Data Mining area, and a way of unsupervised pattern recognition. Cluster analysis is a division of data into groups of similarity, according to established rules. In this project, two recent Swarm Intelligence algorithms are used for cluster analysis of numerical data. The bioinspired optimization methods, Firefly Algorithm and Bat Algorithm, will be adapted to perform the task of data clustering using the partitioning approach. Two different implementations of each algorithm are proposed: the sequential mode in CPU and the parallel programming using GPU with CUDA. The results of both implementations are compared with each other for each of the studied Swarm Intelligence algorithms in order to analyze the performance of the data clustering task.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

SERAPIAO, ADRIANE B. S.; CORREA, GUILHERME S.; GONCALVES, FELIPE B.; CARVALHO, VERONICA O.. Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units. APPLIED SOFT COMPUTING, v. 41, p. 290-304, APR 2016. (13/23027-1, 13/08730-8, 13/08741-0)