developing an algorithm for data mining using clustering genetic algorithm at c++



With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is more than slicing and dicing of data; it is a powerful business intelligence technology that provides knowledge about unknown patterns and events from large databases. Data mining technologies have known to significantly reduce operational costs and improve value of customer relationships. Data mining is a process of inferring knowledge from such huge data.

Clustering is a classical topic in statistical data analysis and machine learning. There are many research work discussing clustering methods. Generally, clustering algorithms can be categorized in several ways. According to the structure of clusters, there are hierarchical methods and partitioning methods. The former builds a hierarchical decomposition upon the data objects, while the latter simply makes a partition for the data. Most partitioning methods are iterative: they start with a set of initial clusters and improve them by iterating a reallocation operation that reassigns the objects.

A genetic algorithm is an iterative procedure that consists of a constant-size population of individuals, each one represented by a finite string of symbols, known as the genome, encoding a possible solution in a given problem space. Evolution has proven to be a very powerful mechanism in finding good solutions to difficult problems. The natural selection can be viewed as an optimization method, which tries to produce adequate solutions to particular environments.

This work makes use of traditional K Means algorithm and Genetic algorithm to cluster the patient’s dataset. The proposed algorithm is named as Genetic Enhanced K Means Clustering. This algorithm works well against all partitioning methods. The performance of this algorithm is comparatively very high compared to other techniques.