A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
ACM Transactions on Knowledge Discovery from Data (TKDD)
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
ACM SIGKDD Explorations Newsletter
Dealing with biometric multi-dimensionality through chaotic neural network methodology
International Journal of Information Technology and Management
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding multiple global linear correlations in sparse and noisy data sets
Knowledge-Based Systems
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In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. In addition, these so-called correlation clusters may have complex relationships between each other. For example, a correlation cluster in a 1-D subspace (forming a line) may be enclosed within one or even several correlation clusters in 2- D superspaces (forming planes). In general, such relationships can be seen as a complex hierarchy that allows multiple inclusions, i.e. clusters may be embedded in several super-clusters rather than only in one. Obviously, uncovering the hierarchical relationships between the detected correlation clusters is an important information gain. Since existing approaches cannot detect such complex hierarchical relationships among correlation clusters, we propose the algorithm ERiC to tackle this problem and to visualize the result by means of a graph-based representation. In our experimental evaluation, we show that ERiC finds more information than state-of-the-art correlation clustering methods and outperforms existing competitors in terms of efficiency.