Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

Abstract

One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GAHDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space. Finally, the result of GA-HDClustering is compared with the traditional clustering algorithm. For comparison different validity matrices such as Sum of squared error (SSE), Within Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI), are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm.

Hybrid Ant Colony Optimization-Genetics Algorithm to Minimize Makespan Flow Shop Scheduling

“…The results of computational testing performed where the first result shows that Tabu Search greatly improve the initial solution given Greedy Algorithm. Genetic algorithm hybridization with Ant Colony Optimization Algorithm to complete the proposed TSP and then evaluated with some data, both random data and sample data from the TSP library [3], [23]. The GA evolution process along with the ant colo-ny's instinct in finding the shortest route to finding food is fully combined and formulated as a new optimization method called GACO.…”