Communications of the ACM
C4.5: programs for machine learning
C4.5: programs for machine learning
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Persistant Linda: Linda + Transactions + Query Processing
Research Directions in High-Level Parallel Programming Languages
An Approach to Fault-tolerant Parallel Processing on Intermittently Idle, Heterogeneous Workstations
FTCS '97 Proceedings of the 27th International Symposium on Fault-Tolerant Computing (FTCS '97)
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Data mining is computationally expensive. Since the benefits of data mining results are unpredictable, organizations may not be willing to buy new hardware for that purpose. We will present a system that enables data mining applications to run in parallel on networks of workstations in a fault-tolerant manner. We will describe our parallelization of a combinatorial pattern discovery algorithm and a classification tree algorithm. We will demonstrate the effectiveness of our system with two real applications: discovering active motifs in protein sequences and predicting foreign exchange rate movement.