Oracle clustering: Dynamic partitioning based on random observations

Reza Zafarani, Ali A. Ghorbani

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

Abstract

In this paper, a new dynamic clustering algorithm based on random sampling is proposed. The algorithm addresses well known challenges in clustering such as Dynamism, Stability, and Scaling. The core of the proposed method is based on the definition of a function, named the Oracle, which can predict whether two random data points belong to the same cluster or not. Furthermore, this algorithm is also equipped with a novel technique for determination of the optimal number of clusters in datasets. These properties add the capabilities of high performance and reducing the effect of scale in datasets to this algorithm. Finally, the algorithm is tuned and evaluated by means of various experiments and in-depth analysis. High accuracy and performance results obtained, demonstrate the competitiveness of our algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Pages27-34
Number of pages8
DOIs
StatePublished - Dec 23 2008
Externally publishedYes
Event20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States
Duration: Nov 3 2008Nov 5 2008

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2
ISSN (Print)1082-3409

Other

Other20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
CountryUnited States
CityDayton, OH
Period11/3/0811/5/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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