MineBench: A benchmark suite for data mining workloads

Ramanathan Narayanan*, Berkin Özisikyilmaz, Joseph Zambreno, Gokhan Memik, Alok Nidhi Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

167 Scopus citations

Abstract

Data mining constitutes an important class of scientific and commercial applications. Recent advances in data extraction techniques have created vast data sets, which require increasingly complex data mining algorithms to sift through them to generate meaningful information. The disproportionately slower rate of growth of computer systems has led to a sizeable performance gap between data mining systems and algorithms. The first step in closing this gap is to analyze these algorithms and understand their bottlenecks. With this knowledge, current computer architectures can be optimized for data mining applications. In this paper, we present MineBench, a publicly available benchmark suite containing fifteen representative data mining applications belonging to various categories such as clustering, classification, and association rule mining. We believe that MineBench will be of use to those looking to characterize and accelerate data mining workloads.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 IEEE International Symposium on Workload Characterization, IISWC - 2006
Pages182-188
Number of pages7
DOIs
StatePublished - 2006
EventIEEE International Symposium on Workload Characterization, IISWC-2006 - San Jose, CA, United States
Duration: Oct 25 2006Oct 27 2006

Publication series

NameProceedings of the 2006 IEEE International Symposium on Workload Characterization, IISWC - 2006

Other

OtherIEEE International Symposium on Workload Characterization, IISWC-2006
Country/TerritoryUnited States
CitySan Jose, CA
Period10/25/0610/27/06

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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