Data clustering, also called unsupervised learn- ing, is a fundamental issue in data mining that is used to understand and mine the structure of an untagged assemblage of data into separate groups based on their similarity. Recent studies have shown that clustering techniques that optimize a single objective may not provide satisfactory result because no single validity measure works well on different kinds of datasets. Moreover, the performance of clustering algorithms degrades with more and more overlaps among clusters in a data set. These facts have motivated us to develop a fuzzy multi-objective particle swarm optimization framework in an innovative fashion for data clustering, termed as FMO- PSO, which is able to deliver more effective results than state-of-the-art clustering algorithms. The key challenge in designing FMOPSO framework for data clustering is how to resolve cluster assignments confusion with such points in the data set which have significant belongingness to more than one cluster. The proposed framework addresses this problem by identification of points having significant membership to multiple classes, excluding them, and re-classifying them into single class assignments. To ascertain the superiority of the proposed algorithm, statistical tests have been performed on a variety of numerical and categorical real life data sets. Our empirical study shows that the performance of the proposed framework (in both terms of efficiency and effectiveness) significantly outperforms the state-of-the-art data clustering algorithms.