A Genetic Algorithm for Clustering on Very Large Data Sets

Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets. The genetic algorithm uses the most time efficient traditional techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.

• Type: Conference
• Year: 2003
• Where: CAINE
• Authors: Jim Gasvoda, Qin Ding
• Keywords: Algorithm, CAINE 2003, CAINE 2007, Data Sets, Genetic Algorithm