A new distributed data mining model based on similarity

Tao Li, Shenghuo Zhu, Mitsunori Ogihara

Research output: Contribution to conferencePaper

21 Scopus citations

Abstract

Distributed Data Mining(DDM) has been very active and enjoying a growing amount attention since its inception. Current DDM techniques regard the distributed data sets as a single virtual table and assume there exists a global model which could be generated if the data were combined/centralized. This paper proposes a similarity-based distributed data mining(SBDDM) framework which explicitly take the differences among distributed sources into consideration. A new similarity measure is introduced and its effectiveness is then evaluated and validated. This paper also illustrates the limitations of current DDM techniques through three concrete case studies. Finally distributed clustering within the SBDDM framework is also discussed.

Original languageEnglish (US)
Pages432-436
Number of pages5
StatePublished - Jul 21 2003
Externally publishedYes
EventProceedings of the 2003 ACM Symposium on Applied Computing - Melbourne, FL, United States
Duration: Mar 9 2003Mar 12 2003

Other

OtherProceedings of the 2003 ACM Symposium on Applied Computing
CountryUnited States
CityMelbourne, FL
Period3/9/033/12/03

Keywords

  • Distributed Data Mining(DDM)
  • SBDDM
  • Similarity

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'A new distributed data mining model based on similarity'. Together they form a unique fingerprint.

  • Cite this

    Li, T., Zhu, S., & Ogihara, M. (2003). A new distributed data mining model based on similarity. 432-436. Paper presented at Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, FL, United States.