Efficiently detecting arbitrary shaped clusters in image databases

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/sup +/, a novel approach to apply wavelet-based techniques for clustering high-dimensional data. Using a hash-based data structure to represent the data set, we offer a detailed technique to apply a 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 data sets.