Procedure 1. Constructive Phase(x, M). 1: Read data(); 2: for i = 1, ... , M - 1 do 3: if NPlease...

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Procedure 1. Constructive Phase(x, M). 1: Read data(); 2: for i = 1, ... , M - 1 do 3: if N
Please follow the contents described below:

1- Name of the Article.
2- Introduction: this part should describe the project and importance of achieving the tasks regarding it.
3- Results or outcomes: this part should clearly identify the results or outcomes of the project. Benefits associated with the projects should be clearly identified.
4- Methodology: this part is the longest section. Describe the tools used for the project in detail. What kind of resources were required to achieve the project? What were the limitations that prevented further development?
5- Future studies: this part should indicate further improvements of the project, imply about the future work.
6- Acknowledgement: this part should define the contributions of each team member to the project.
Note: The project report should be 1-4 pages long. Avoid any unnecessary information and try to use English precisely.

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Computers & Operations Research 37 (2010) 1381--1388 Contents lists available at ScienceDirect Computers&OperationsResearch journal homepage: www.elsevier.com/locate/cor InvestigationofanewGRASP-basedclusteringalgorithmappliedtobiologicaldata * MariáC.V.Nascimento ,FranklinaM.B.Toledo,AndréC.P.L.F.deCarvalho Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, São Carlos-SP, CEP 13560-970, Brazil ARTICLE INFO ABSTRACT Available online 5 March 2009 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 Keywords: data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering Clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more ade- GRASP quate for particular datasets. This paper presents a mathematical formulation to support the creation of Gene expression data consistent clusters for biological data. Moreover, it shows a clustering algorithm to solve this formulation Bioinformatics 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 con- firmed statistically. © 2009 Elsevier Ltd. All rights reserved. applied to several domains, like natural language processing [2], 1. Introduction galaxy formation [3] and image segmentation [4]. Surveys and reviews on clustering algorithms and their application to different In the last years there has been a considerable growth of the domains can be found in [5,6]. In particular, a large number of ex- amount of biological data available in several domains. Two of these periments using clustering algorithms to group biological data have domains are proteomics, which studies properties found in proteins, been...

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David · Accepted Answer

Grasp based Clustering algorithm for Biological data
Introduction:
Presence of large amount of biological data can be used to extract some useful knowledge 
with the help of data analysis technique. One of the successful data analysis techniques is 
clustering algorithm that performs data analysis by organizing similar data object into groups. 
Clustering algorithm is a powerful tool for classification of groups and sub-groups in 
biological data. It works by grouping most similar data in one group and dissimilar data in 
other groups. It can be categorized as two approaches: hierarchical and partitioning. 
Hierarchical approach produces nested partitions. And partitioning approach produces 
partitions based on optimization of given objective or criteria. Various metaheuristics can also 
be used in clustering algorithm. These metaheuristics help producing well valued solution. 
One of them is GRASP (Greedy Randomized Adaptive Search Procedure), a search algorithm 
that first build an initial solution using some greedy process and then applying some local 
search operation on each solution previously built. GRASP is very effective in presenting an 
effective solution in a very less time.
Results or outcomes
It clearly finds out the partitions of datasets very optimally. Project consists of finding out an 
optimal solution for datasets using GRASP based clustering algorithm and CPLEX version 
and other clustering algorithms and then comparing their results, i.e.

Procedure 1. Constructive Phase(x, M). 1: Read data(); 2: for i = 1, ... , M - 1 do 3: if N Please follow the contents described below: 1- Name of the Article. 2- Introduction: this part should...

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