CoDA: Interactive Cluster Based Concept Discovery

Summary: CoDA offers a workflow for discovering concepts from subspace clusters, guiding analysts through iterative suggestion and refinement. Its core is a concept-driven visual presentation of subspace patterns that lets knowledge shape concepts. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10206
Venue: VLDB
Year: 2010
Pagerank: -
Overall Rank: 13,718 | 5.89%
DOI: 10.14778/1920841.1921058

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Stephan Günnemann (RWTH Aachen University)
2. Ines Färber (RWTH Aachen University)
3. Hardy Kremer (RWTH Aachen University)
4. Thomas Seidl (RWTH Aachen University)

BibTeX Citation

@article{gunnemann_vldb10,
        title = {{CoDA: Interactive Cluster Based Concept Discovery}},
        author = {Günnemann, Stephan and Färber, Ines and Kremer, Hardy and Seidl, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {2},
        doi = {10.14778/1920841.1921058},
        url = {https://doi.org/10.14778/1920841.1921058},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
8,778	Evaluating Clustering in Subspace Projections of High Dimensional Data	2009	VLDB	5.3753402e-05

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