Description - Advances in Distributed and Parallel Knowledge Discovery by Hillol Kargupta
Knowledge discovery and data mining (KDD) deals with the extraction of associations, classifiers, clusters and other patterns from data. Network-based distributed computing environments have introduced an important dimension to this problem - distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which is not always feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks. When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques address this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.
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