Efficiently detecting arbitrary shaped clusters in image databases

Dantong Yu, Surojit Chatterjee, Aidong Zhang

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

Image databases contain data with high dimensions. Finding interesting patterns in these databases poses a very challenging problem because of the scalability, lack of domain knowledge and complex structures of the embedded clusters. High dimensionality adds severely to the scalability problem. In this paper, we introduce WaveCluster+, a novel approach to apply wavelet-based techniques for clustering high dimensional data. Using a hash-based data structure to represent the dataset, we offer a detailed technique to apply wavelet transform on the hashed feature space. We demonstrate that the cost of clustering can be reduced dramatically yet maintaining all the advantages of wavelet-based clustering. This hash-based data representation can be applied for any grid-based clustering approaches. The experimental results show the effectiveness and efficiency of our method on high dimensional datasets.

Original languageEnglish (US)
Pages (from-to)187-194
Number of pages8
JournalProceedings of the International Conference on Tools with Artificial Intelligence
StatePublished - Dec 1 1999
Externally publishedYes
EventProceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '99) - Chicago, IL, USA
Duration: Nov 9 1999Nov 11 1999

All Science Journal Classification (ASJC) codes

  • Software

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