Investigation of a new GRASP-based clustering algorithm applied to biological data

Authors:
Mariá C. V. Nascimento;Franklina M. B. Toledo;André C. P. L. F. de Carvalho
Affiliations:
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, São Carlos-SP, CEP 13560-970, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, São Carlos-SP, CEP 13560-970, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, São Carlos-SP, CEP 13560-970, Brazil
Venue:
Computers and Operations Research
Year:
2010

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Abstract

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover, it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically.