Free parallel data mining

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish (US)
Pages (from-to)541-543
Number of pages3
JournalSIGMOD Record (ACM Special Interest Group on Management of Data)
Issue number2
StatePublished - 1998
EventProceedings of the ACM SIGMOD International Conference on Management of Data - Seattle, WA, USA
Duration: Jun 1 1998Jun 4 1998

ASJC Scopus subject areas

  • Software
  • Information Systems


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