Free parallel data mining

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish (US)
Pages (from-to)541-543
Number of pages3
JournalSIGMOD Record (ACM Special Interest Group on Management of Data)
Volume27
Issue number2
DOIs
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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