Abstract
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.
Original language | English (US) |
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Pages (from-to) | 541-543 |
Number of pages | 3 |
Journal | SIGMOD Record (ACM Special Interest Group on Management of Data) |
Volume | 27 |
Issue number | 2 |
DOIs | |
State | Published - 1998 |
Event | Proceedings of the ACM SIGMOD International Conference on Management of Data - Seattle, WA, USA Duration: Jun 1 1998 → Jun 4 1998 |
ASJC Scopus subject areas
- Software
- Information Systems