TAILIEUCHUNG - A review on clustering with genetic algorithms

This paper presents a review on genetic algorithms based clustering techniques. Clustering is one of the most important tasks of data mining for exploring data sets. It can be used to extract useful and hidden information from the datasets. Clustering techniques have a large area of applications including bioinformatics, web use data analysis and image analysis etc. | ISSN:2249-5789 Mamta Mor et al , International Journal of Computer Science & Communication Networks,Vol 4(3),94-98 A Review on Clustering with Genetic Algorithms MamtaMor Poonam Gupta OITM, Dept. of CSE, GJUS&TOITM, Dept. of CSE, GJUS&T mamtamor12121990@ poonamjindal3@ Abstract This paper presents a review on genetic algorithms based clustering techniques. Clustering is one of the most important tasks of data mining for exploring data sets. It can be used to extract useful and hidden information from the datasets. Clustering techniques have a large area of applications including bioinformatics, web use data analysis and image analysis etc. Traditional clustering algorithms applied to datasets most of the times result in sub-optimal solution due to large search space, so evolutionary algorithms particularly genetic algorithms are best suited for the clustering tasks. The capability of Genetic algorithms is applied to find optimally disjoint partitions and proper number of clusters for a dataset. 1. Introduction Data mining is theprocess of extracting useful and hidden information or knowledge from data sets. The information so extracted can be used to improve the decision making capabilities of a company or an organization[1][2][3].Data mining consists of six basic types of tasks which are Anomaly detection, Association rule learning, Clustering, Classification, Regression and Summarization. Clustering is one of the important tasks of data mining. Clustering is the unsupervised classification of data objects into groups or clusters. Clustering is defined as the task of grouping objects in such a way that the objects in the same group/cluster share some similar properties/ non-GA-based algorithms such as K-means and Fuzzy-c-means havebeen used for the clustering tasks [7]. One of the main goals of clustering algorithms is to find „natural‟ groups in the dataset along with partitioning the data into those natural groups [2][4]. But none

• Kỹ thuật lập trình
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• A review on clustering with genetic algorithms
• Clustering with genetic algorithms
• Data analysis and image analysis
• Data mining for exploring data sets
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