Clustering is a significant data mining task which partitions datasets based on similarities among data. This technique plays a very important role in the rapidly growing field known as exploratory data analysis. A key difficulty of effective clustering is to define proper grouping criteria that reflect fundamentally different aspects of a good clustering solution such as compactness and separation of clusters. Moreover, in the conventional clustering algorithms only a single criterion is considered that may not conform to the diverse and complex shapes of the underlying clusters. In this study, partitional clustering is defined as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. A comprehensive experimental study is conducted and the obtained results are compared with the results of four other state-of-the-art clustering techniques. It is shown that the proposed algorithm can achieve the optimal number of clusters, is robust and outperforms, in most cases, the other methods on the selected benchmark datasets.