Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reordering the Reorderable Matrix as an Algorithmic Problem
Diagrams '00 Proceedings of the First International Conference on Theory and Application of Diagrams
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
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Large data resources are ubiquitous in science and business. For these domains, an intuitive view on the data is essential to fully exploit the hidden knowledge. Often, these data can be semantically structured by concepts. Since the determination of concepts requires a thorough analysis of the data, data mining methods have to be applied. In the field of subspace clustering, some techniques have recently shown to be effective for this task. Although these methods generate concept-based patterns, the user has to provide domain knowledge to gain reasonable concepts out of the data. Our demonstration CoDA (Concept Determination and Analysis) is a tool that supports the user in the final step of concept definition. More concretely, the user is guided through an iterative, interactive process in which concepts are suggested, analyzed, and potentially refined. The core aspect of CoDA is an intuitive, concept-driven presentation of subspace clusters such that concepts can be visually captured.