Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Relevant Subspace Clustering : Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. / Müller, Emmanuel; Assent, Ira; Günnemann, Stephan; Krieger, Ralph; Seidl, Thomas.

Proc. IEEE International Conference on Data Mining (ICDM 2009). 2009. p. 377-386.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Müller, E, Assent, I, Günnemann, S, Krieger, R & Seidl, T 2009, Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. in Proc. IEEE International Conference on Data Mining (ICDM 2009). pp. 377-386. https://doi.org/10.1109/ICDM.2009.10

APA

Müller, E., Assent, I., Günnemann, S., Krieger, R., & Seidl, T. (2009). Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. In Proc. IEEE International Conference on Data Mining (ICDM 2009) (pp. 377-386) https://doi.org/10.1109/ICDM.2009.10

CBE

Müller E, Assent I, Günnemann S, Krieger R, Seidl T. 2009. Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. In Proc. IEEE International Conference on Data Mining (ICDM 2009). pp. 377-386. https://doi.org/10.1109/ICDM.2009.10

MLA

Müller, Emmanuel et al. "Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data". Proc. IEEE International Conference on Data Mining (ICDM 2009). 2009, 377-386. https://doi.org/10.1109/ICDM.2009.10

Vancouver

Müller E, Assent I, Günnemann S, Krieger R, Seidl T. Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. In Proc. IEEE International Conference on Data Mining (ICDM 2009). 2009. p. 377-386 https://doi.org/10.1109/ICDM.2009.10

Author

Müller, Emmanuel ; Assent, Ira ; Günnemann, Stephan ; Krieger, Ralph ; Seidl, Thomas. / Relevant Subspace Clustering : Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. Proc. IEEE International Conference on Data Mining (ICDM 2009). 2009. pp. 377-386

Bibtex

@inproceedings{d796fef609bc4c81bce90d1d6b0477d5,
title = "Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data",
abstract = "Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.",
author = "Emmanuel M{\"u}ller and Ira Assent and Stephan G{\"u}nnemann and Ralph Krieger and Thomas Seidl",
year = "2009",
doi = "10.1109/ICDM.2009.10",
language = "English",
isbn = "978-0-7695-3895-2",
pages = "377--386",
booktitle = "Proc. IEEE International Conference on Data Mining (ICDM 2009)",

}

RIS

TY - GEN

T1 - Relevant Subspace Clustering

T2 - Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data

AU - Müller, Emmanuel

AU - Assent, Ira

AU - Günnemann, Stephan

AU - Krieger, Ralph

AU - Seidl, Thomas

PY - 2009

Y1 - 2009

N2 - Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.

AB - Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.

U2 - 10.1109/ICDM.2009.10

DO - 10.1109/ICDM.2009.10

M3 - Article in proceedings

SN - 978-0-7695-3895-2

SP - 377

EP - 386

BT - Proc. IEEE International Conference on Data Mining (ICDM 2009)

ER -