Improved Clustering for Categorical Data with Genetic Algorithm

Abstract

Clustering is the most significant unsupervised learning where the aim is to partition the data set into uniform groups called clusters. Many real-world data sets often contain categorical values, but many clustering algorithms work only on numeric values which limits its use in data mining. The k-modes algorithm is one of the very effective for proper partitions of categorical data sets, though the algorithm stops at locally optimum solution as depended on initial cluster centres. Proposed algorithm utilizes the genetic algorithm (GA) to optimize the k-modes clustering algorithm. The reason is, considering noise as cluster centres gives the high cost which will not fit for the next iteration and also not gets stuck to the suboptimal solutions. The superiority of proposed algorithm is demonstrated for several real-life data sets in terms of accuracy and proves it is efficient and can reveal encouraging results especially for the large datasets.