Clustering is a popular data mining technique for grouping a set of objects into clusters so that objects in one cluster are very similar and objects in different clusters are quite distinct. K-means (KM) algorithm is an efficient data clustering method as it is simple in nature and has linear time complexity. However, it has possibilities of convergence to local minima in addition to dependence on initial cluster centers. Artificial Bee Colony (ABC) algorithm is a stochastic optimization method inspired by intelligent foraging behavior of honey bees. In order to make use of merits of both algorithms, a hybrid algorithm (MABCKM) based on modified ABC and KM algorithm is proposed in this paper. The solutions produced by modified ABC are treated as initial solutions for the KM algorithm. The performance of the proposed algorithm is compared with the ABC and KM algorithms on various data sets from the UCI repository. The experimental results prove the superiority of the MABCKM algorithm for data clustering applications.