Morpheus: Interactive Exploration of Subspace Clustering

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

Standard

Morpheus : Interactive Exploration of Subspace Clustering. / Müller, Emmanuel; Assent, Ira; Krieger, Ralph; Jansen, Timm; Seidl, Thomas.

Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008. p. 1089-1092.

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

Harvard

Müller, E, Assent, I, Krieger, R, Jansen, T & Seidl, T 2008, Morpheus: Interactive Exploration of Subspace Clustering. in Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 1089-1092. https://doi.org/10.1145/1401890.1402026

APA

Müller, E., Assent, I., Krieger, R., Jansen, T., & Seidl, T. (2008). Morpheus: Interactive Exploration of Subspace Clustering. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1089-1092) https://doi.org/10.1145/1401890.1402026

CBE

Müller E, Assent I, Krieger R, Jansen T, Seidl T. 2008. Morpheus: Interactive Exploration of Subspace Clustering. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 1089-1092. https://doi.org/10.1145/1401890.1402026

MLA

Müller, Emmanuel et al. "Morpheus: Interactive Exploration of Subspace Clustering". Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008, 1089-1092. https://doi.org/10.1145/1401890.1402026

Vancouver

Müller E, Assent I, Krieger R, Jansen T, Seidl T. Morpheus: Interactive Exploration of Subspace Clustering. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008. p. 1089-1092 https://doi.org/10.1145/1401890.1402026

Author

Müller, Emmanuel ; Assent, Ira ; Krieger, Ralph ; Jansen, Timm ; Seidl, Thomas. / Morpheus : Interactive Exploration of Subspace Clustering. Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008. pp. 1089-1092

Bibtex

@inproceedings{2a7b4b80238340558737358addeaeff9,
title = "Morpheus: Interactive Exploration of Subspace Clustering",
abstract = "Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery. Visualization techniques exist for traditional clustering in low dimensional spaces. In high dimensional data, clusters typically only exist in subspace projections. This subspace clustering, however, lacks interactive visualization tools. Challenges arise from typically large result sets in different subspace projections that hinder comparability, visualization and understandability.In this work, we describe Morpheus, a tool that supports the knowledge discovery process through visualization and interactive exploration of subspace clusterings. Users may browse an overview of the entire subspace clustering, analyze subspace cluster characteristics in-depth and zoom into object groupings. Bracketing of different parameter settings enables users to immediately see the effects of parameters and to provide feedback to further improve the subspace clustering. Furthermore, Morpheus may serve as a teaching and exploration tool for the data mining community to visually assess different subspace clustering paradigms.",
author = "Emmanuel M{\"u}ller and Ira Assent and Ralph Krieger and Timm Jansen and Thomas Seidl",
year = "2008",
doi = "10.1145/1401890.1402026",
language = "English",
isbn = "978-1-60558-193-4",
pages = "1089--1092",
booktitle = "Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining",

}

RIS

TY - GEN

T1 - Morpheus

T2 - Interactive Exploration of Subspace Clustering

AU - Müller, Emmanuel

AU - Assent, Ira

AU - Krieger, Ralph

AU - Jansen, Timm

AU - Seidl, Thomas

PY - 2008

Y1 - 2008

N2 - Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery. Visualization techniques exist for traditional clustering in low dimensional spaces. In high dimensional data, clusters typically only exist in subspace projections. This subspace clustering, however, lacks interactive visualization tools. Challenges arise from typically large result sets in different subspace projections that hinder comparability, visualization and understandability.In this work, we describe Morpheus, a tool that supports the knowledge discovery process through visualization and interactive exploration of subspace clusterings. Users may browse an overview of the entire subspace clustering, analyze subspace cluster characteristics in-depth and zoom into object groupings. Bracketing of different parameter settings enables users to immediately see the effects of parameters and to provide feedback to further improve the subspace clustering. Furthermore, Morpheus may serve as a teaching and exploration tool for the data mining community to visually assess different subspace clustering paradigms.

AB - Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery. Visualization techniques exist for traditional clustering in low dimensional spaces. In high dimensional data, clusters typically only exist in subspace projections. This subspace clustering, however, lacks interactive visualization tools. Challenges arise from typically large result sets in different subspace projections that hinder comparability, visualization and understandability.In this work, we describe Morpheus, a tool that supports the knowledge discovery process through visualization and interactive exploration of subspace clusterings. Users may browse an overview of the entire subspace clustering, analyze subspace cluster characteristics in-depth and zoom into object groupings. Bracketing of different parameter settings enables users to immediately see the effects of parameters and to provide feedback to further improve the subspace clustering. Furthermore, Morpheus may serve as a teaching and exploration tool for the data mining community to visually assess different subspace clustering paradigms.

U2 - 10.1145/1401890.1402026

DO - 10.1145/1401890.1402026

M3 - Article in proceedings

SN - 978-1-60558-193-4

SP - 1089

EP - 1092

BT - Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

ER -