Collaborative artificial bee colony k-mean clustering algorithm for mixed data set

Abstract

Data mining techniques are used to extract useful patterns from a large data set. k-mean algorithm is one of the most famous partitioning clustering algorithm. But, Euclidean distance is sensitive to outliers and is suitable to only numeric values. Real time datasets have mixed attribute values, missing values and measurements are not in the standard format.The proposed algorithm extends the ability of the kmean algorithm to use a mixed simil arity measure to find the similarity between data objects for clustering mixed datasets.For imp uting missing values, correlation based data imputation is used.In addition, k-mean output depends on the initial cluster centre and local optima suffers from the number of clusters(k). In order to improve the efficiency of the k-mean algorithm, Artificial Bee Colony Optimization (ABC) based clustering algorithm is suggested. ABC is successful at exploring the search space, but endures in leveraging the search space. Collaborative search is used to amplify the search quality of bees to amplify the search quality of bees employees. To determine the number of clusters for the given data set, the Elbow method is used. In order to evaluate the outcome of the proposed algorithm, real time datasets are used. The results showed that the proposed method performs well compared to comparative algorithms.