| Document Date: 2013-05-17 06:56:48
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/ Facility OR library / University of Konstanz Box / / IndustryTerm parallel search algorithm / less greedy algorithm / classic greedy algorithm / greedy set covering algorithm / candidate refinement solutions / analysis algorithms / refinement operator / real selection operator / normal algorithm / lookahead-based algorithms / parallel computing resources / neural network / greedy algorithms / greedy selection operator / easy algorithms / faster then conventional algorithms / parallel search / data mining methods / greedy algorithm / polynomial-time algorithm / data mining / elaborate mining algorithms / efficient mining / data mining algorithms / classic heuristic search process / data mining research / heuristic data mining algorithms / sequential algorithms / depth-k look-ahead algorithm / beam search / search algorithm / selection operator / few data mining algorithms / parallel commodity hardware / / Organization University of Konstanz Box / / Person Faster Zaenal Akbar / Z. Akbar / V / Violeta N. Ivanova / Michael R. Berthold Nycomed-Chair / / Position representative / / Technology lookahead-based algorithms / elaborate mining algorithms / covering algorithm / Bioinformatics / parallel search algorithm / less greedy algorithm / neural network / normal algorithm / search algorithm / few data mining algorithms / Set Cover algorithm / classic greedy algorithm / depth-k look-ahead algorithm / polynomial-time algorithm / data mining algorithms / greedy algorithm / clustering algorithm / all relevant algorithms / presumably easy algorithms / Data Mining / analysis algorithms / faster then conventional algorithms / heuristic data mining algorithms / Machine Learning / second algorithm / greedy algorithms / greedy set covering algorithm / tuning data mining algorithms / widened algorithm / existing sequential algorithms / / URL http /
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Parallel Data Mining Revisited. Better, Not Faster Zaenal Akbar, Violeta N. Ivanova, and Michael R. Berthold Nycomed-Chair for Bioinformatics and Information Mining Dept. of Computer and Information Science, University o