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Data partitioning methods such as bagging and boosting have been extensively used in multiple classifier systems. These methods have shown a great potential for improving classification accuracy. This study is concerned with the analysis of training data distribution and its impact on the performance of multiple classifier systems. In this study, several feature-based and class-based measures are proposed. These measures can be used to estimate statistical characteristics of the training partitions. To assess the effectiveness of different types of training partitions, we generated a large number of disjoint training partitions with distinctive distributions. Then, we empirically assessed these training partitions and their impact on the performance of the system by utilizing the proposed feature-based and class-based measures. We applied the findings of this analysis and developed a new partitioning method called "Clustering, Declustering, and Selection" (CDS). This study presents a comparative analysis of several existing data partitioning methods including our proposed CDS approach.