Document Clustering has become an increasingly important task in the area of data mining and information retrieval. With growing data volumes, CPU—and memory-efficient techniques for clustering algorithms are receiving considerable attention in the research community. To deal with huge amounts of data (e.g., documents from Wikipedia or CiteSeerX which are several GB in size), distributed clustering techniques have been designed to provide scalable and flexible approaches. We study the problem of document clustering in the area of Entity Matching, where documents from various data sources are matched together. More specifically, we focus on a common optimization technique called blocking which reduces the enormous search space by clustering the data sources into smaller groups and processes comparisons only within a group. In this article, we describe our experiences and findings in applying the MapReduce framework to deal with huge bibliographic data sets and to provide a flexible, scalable and easy-to-use blocking technique to reduce the search space for Entity Matching.
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