{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:12:44Z","timestamp":1770984764543,"version":"3.50.1"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2007,12,1]],"date-time":"2007-12-01T00:00:00Z","timestamp":1196467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:p>To gain insight into today's large data resources, data mining extracts interesting patterns. To generate knowledge from patterns and benefit from human cognitive abilities, meaningful visualization of patterns are crucial. Clustering is a data mining technique that aims at grouping data to patterns based on mutual (dis)similarity. For high dimensional data, subspace clustering searches patterns in any subspace of the attributes as patterns are typically obscured by many irrelevant attributes in the full space. For visual analysis of subspace clusters, their comparability has to be ensured. Existing subspace clustering approaches, however, lack interactive visualization and show bias with respect to the dimensionality of subspaces.<\/jats:p>\n          <jats:p>In this work, dimensionality unbiased subspace clustering and a novel distance function for subspace clusters are proposed. We suggest two visualization techniques that allow users to browse the entire subspace clustering, to zoom into individual objects, and to analyze subspace cluster characteristics in-depth. Bracketing of different parameter settings enable users to immediately see the effect of parameters on their data and hence to choose the best clustering result for further analysis. Usage of user analysis for feedback to the subspace clustering algorithm directly improves the subspace clustering. We demonstrate our visualization techniques on real world data and confirm results through additional accuracy measurements and comparison with existing subspace clustering algorithms.<\/jats:p>","DOI":"10.1145\/1345448.1345451","type":"journal-article","created":{"date-parts":[[2008,2,28]],"date-time":"2008-02-28T14:02:33Z","timestamp":1204207353000},"page":"5-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["VISA"],"prefix":"10.1145","volume":"9","author":[{"given":"Ira","family":"Assent","sequence":"first","affiliation":[{"name":"RWTH Aachen University, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph","family":"Krieger","sequence":"additional","affiliation":[{"name":"RWTH Aachen University, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"RWTH Aachen University, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Seidl","sequence":"additional","affiliation":[{"name":"RWTH Aachen University, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/276304.276314"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2006.153"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.49"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/645503.656271"},{"key":"e_1_2_1_5_1","volume-title":"Pattern Classification","author":"Duda R.","year":"2001","unstructured":"R. Duda , P. Hart , and D. Stork . Pattern Classification . Wiley , New York , 2001 . R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley, New York, 2001."},{"key":"e_1_2_1_6_1","first-page":"14863","volume-title":"Proc","author":"Eisen M.","year":"1998","unstructured":"M. Eisen , P. Spellman , P. Brown , and D. Botstein . Cluster analysis and display of genome-wide expression patterns . In Proc . National Academy of Science of the USA, volume 95 , pages 14863 -- 14868 , 1998 . M. Eisen, P. Spellman, P. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. In Proc. National Academy of Science of the USA, volume 95, pages 14863--14868, 1998."},{"key":"e_1_2_1_7_1","first-page":"226","volume-title":"Proc. 2nd Intl. Conf. on Knowledge Discovery and Data Mining","author":"Ester M.","year":"1996","unstructured":"M. Ester , H. Kriegel , J. Sander , and X. Xu . A density-based algorithm for discovering clusters in large spatial databases . In Proc. 2nd Intl. Conf. on Knowledge Discovery and Data Mining , pages 226 -- 231 , Portland, Oregon, USA , 1996 . M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In Proc. 2nd Intl. Conf. on Knowledge Discovery and Data Mining, pages 226--231, Portland, Oregon, USA, 1996."},{"key":"e_1_2_1_8_1","volume-title":"Information visualization in data mining and knowledge discovery","author":"Fayyad U.","year":"2002","unstructured":"U. Fayyad , G. G. Grinstein , and A. Wierse , editors . Information visualization in data mining and knowledge discovery . Morgan Kaufmann Publishers Inc ., San Francisco, CA, USA, 2002 . U. Fayyad, G. G. Grinstein, and A. Wierse, editors. Information visualization in data mining and knowledge discovery. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2002."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2003.1207445"},{"key":"e_1_2_1_10_1","volume-title":"Computer Graphics","author":"Foley J. D.","year":"1996","unstructured":"J. D. Foley , A. van Dam , S. K. Feiner , and J. F. Hughes . Computer Graphics ( 2 nd ed. in C): Principles and Practice. Addison-Wesley , Boston, MA, USA, 1996 . J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes. Computer Graphics (2nd ed. in C): Principles and Practice. Addison-Wesley, Boston, MA, USA, 1996.","edition":"2"},{"key":"e_1_2_1_11_1","volume-title":"Morgan Kaufmann","author":"Han J.","year":"2001","unstructured":"J. Han and M. Kamber . Data Mining: Concepts and Techniques . Morgan Kaufmann , San Francisco, CA, USA , 2001 . J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, CA, USA, 2001."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972740.23"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.981847"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/IV.2006.31"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/1182635.1164203"},{"key":"e_1_2_1_16_1","volume-title":"Visual and Spatial Analysis - Advances in Data Mining, Reasoning, and Problem Solving","author":"Kovalerchuk B.","year":"2004","unstructured":"B. Kovalerchuk and J. Schwing . Visual and Spatial Analysis - Advances in Data Mining, Reasoning, and Problem Solving . Springer , 2004 . B. Kovalerchuk and J. Schwing. Visual and Spatial Analysis - Advances in Data Mining, Reasoning, and Problem Solving. Springer, 2004."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.5"},{"key":"e_1_2_1_18_1","first-page":"1","volume-title":"Psychometrika","author":"Kruskal J. B.","year":"1964","unstructured":"J. B. Kruskal . Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. In Psychometrika , volume 29 , pages 1 -- 27 . Springer New York , 1964 . J. B. Kruskal. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. In Psychometrika, volume 29, pages 1--27. Springer New York, 1964."},{"key":"e_1_2_1_20_1","volume-title":"UCI repository of machine learning databases","author":"Newman D.","year":"2006","unstructured":"D. Newman , S. Hettich , C. Blake , and C. Merz . UCI repository of machine learning databases , 2006 . D. Newman, S. Hettich, C. Blake, and C. Merz. UCI repository of machine learning databases, 2006."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007731"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/989863.989893"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/1032649.1033453"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-3324-9"},{"key":"e_1_2_1_25_1","volume-title":"Visual Data Mining: Techniques and Tools for Data Visualization and Mining","author":"Soukup T.","year":"2002","unstructured":"T. Soukup and I. Davidson . Visual Data Mining: Techniques and Tools for Data Visualization and Mining . Wiley , 2002 . T. Soukup and I. Davidson. Visual Data Mining: Techniques and Tools for Data Visualization and Mining. Wiley, 2002."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.01.005"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1345448.1345451","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1345448.1345451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:22:21Z","timestamp":1750278141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1345448.1345451"}},"subtitle":["visual subspace clustering analysis"],"short-title":[],"issued":{"date-parts":[[2007,12]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2007,12]]}},"alternative-id":["10.1145\/1345448.1345451"],"URL":"https:\/\/doi.org\/10.1145\/1345448.1345451","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"value":"1931-0145","type":"print"},{"value":"1931-0153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,12]]},"assertion":[{"value":"2007-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}