Distributed Clustering for Data Sources with Diverse Schema

Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some diseases, one may wish to integrate data gathered from many different hospitals. Analyzing and mining these distributed heterogeneous data sources require distributed machine learning and data mining technique In this paper, a Modified Distributed Combining Algorithm is proposed to cluster disparate data sources having diverse, possibly overlapping set of features and also need not share objects. First, all objects located at local sites are grouped using K-Means/Fuzzy C-Means clustering algorithm and resulting centroid is considered as local models. Then, the set of centroids are transformed into unified structure and optimum values are assigned to missing attributes. Finally, global cluster centroid is computed to identify global cluster model based on cluster ensemble and centroid mapping. The experiments are carried out for various datasets of UCI machine learning data repository in order to achieve the efficiency of the proposed algorithm.