BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
High-dimensional index structures database support for next decade's applications (tutorial)
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Semantic clustering and querying on heterogeneous features for visual data
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Color Clustering Techniques for Color-Content-Based Image Retrieval from Image Databases
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
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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.