The purpose of this review paper to discuss the current state of the art of data mining and data intensive computing and the opportunities and challenges for the future. The focus of the discussions is on mining large, massive, and distributed data sets. Data mining attempts to formulate, analyze and implement basic induction processes that facilitate the extraction of meaningful information and knowledge from unstructured data. Data mining is also a promising computational paradigm that enhances traditional approaches to discovery and increases the opportunities for breakthroughs in the understanding of complex physical and biological systems. Researchers from many intellectual communities contributed to machine learning, statistics, databases, visualization and graphics, optimization, computational mathematics, and the theory of algorithms. The paper addresses the major challenges such as developing algorithms and systems to mine large, massive and high dimensional data sets; developing algorithms and systems to mine new types of data; developing algorithms, protocols, and other infrastructure to mine distributed data; and improving the ease of use of data mining systems; developing appropriate privacy and security models for data mining.
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