Due to the large volume of data generated by the growth of applications that provide new information, both in volume and variety, more efficient techniques are required to classify and processes them. A widely used technique is data grouping whose aim is to extract characteristics of the entities dividing them into homogeneous and/or well separated subsets. Many different criteria can be used to express the data separation. Among them, a commonly used criteria is the minimun sum-of-squares clustering (MSSC). The data clustering problem by MSSC is NP-hard, then heuristics are extremely useful techniques for this type of problem. This paper proposes a new heuristic, which has as main characteristic concern for the position of centroids during the construction of the solution to solve the MSSC problem. Preliminary results already show improvements of the proposed algorithm on well established heuristics in the literature, J-Means. In addition, this work aims to use the proposed algorithm as local search in metaheuristic Variable Neighborhood Search (VNS), and to combine the two heuristics compared in order to overcome the best solutions known in the literature.