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Issue No. 04 - April (2009 vol. 21)
ISSN: 1041-4347
pp: 465-478
Ran Wolff , Haifa University, Haifa
Kanishka Bhaduri , University of Maryland, Baltimore County, Baltimore
Hillol Kargupta , University of Maryland, Baltimore County, Baltimore
In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the \emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, $k$-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient \emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its $k$-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.
Data mining, Mining methods and algorithms, Distributed databases, Peer to Peer Data Mining, Distributed systems, Systems and Software, Information Storage and Retrieval, Information Technology

H. Kargupta, R. Wolff and K. Bhaduri, "A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 465-478, 2008.
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