Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
AutoPart: parameter-free graph partitioning and outlier detection
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Shader space navigator: a turbo for an intuitive and effective shading process
Proceedings of the 2009 ACM symposium on Applied Computing
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
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This paper proposes an approach of hierarchical image data organization for effective image retrieval. Our approach is basically based on the Cross-Association (CA) that was originally devised for uncovering hidden communities in data without requiring any parameters. We first modify the CA to be appropriate for the clustering context, and propose a hierarchical clustering algorithm based on the modified version of CA. Then, we propose a novel algorithm for outlier detection that is well matched to the CA framework. We perform extensive experiments to show the effectiveness of our clustering algorithm and also our outlier detection algorithm. We also demonstrate the results obtained by applying our algorithms to real-world data.