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FpVAT: a visual analytic tool for supporting frequent pattern mining

Authors:

Carson Kai-Sang Leung,

Christopher L. CarmichaelAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 11, Issue 2

Pages 39 - 48

https://doi.org/10.1145/1809400.1809407

Published: 27 May 2010 Publication History

Abstract

As frequent pattern mining plays an essential role in many knowledge discovery and data mining (KDD) tasks, numerous algorithms for finding frequent patterns have been proposed over the past 15 years. However, most of these algorithms return the mining results in the form of textual lists containing frequent patterns showing those frequently occurring sets of items. It is well known that "a picture is worth a thousand words". The use of visual representation can enhance the user's understanding of the inherent relations in a collection of frequent patterns. In this paper, we develop a simple yet useful visual analytic tool for supporting frequent pattern mining called FpVAT. Such a visual analytic tool consists of two modules: One module gives users an overview so that they can derive insight from a massive amount of raw data; another module enables users to perform analytical reasoning on the mining results via interactive visual interfaces so that users can detect the expected frequent patterns and discover the unexpected frequent patterns. As a visual analytic tool, our FpVAT is equipped with several interactive features for effective visual support in the data analysis and KDD process for various real-life applications.

References

[1]

R. Agrawal & R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. VLDB 1994, pp. 487--499.

Digital Library

[2]

C. Ahlberg. Spotfire: An information exploration environment. SIGMOD Record, 25(4), pp. 25--29, 1996.

Digital Library

[3]

M. Ankerst, C. Elsen, M. Ester & H.-P. Kriegel. Visual classification: An interactive approach to decision tree construction. In Proc. KDD 1999, pp. 392--396.

Digital Library

[4]

R.J. Bayardo Jr. Efficiently mining long patterns from databases. In Proc. ACM SIGMOD 1998, pp. 85--93.

Digital Library

[5]

J. Blanchard, F. Guillet & H. Briand. Interactive visual exploration of association rules with rule-focusing methodology. KAIS, 13(1), pp. 43--75, 2007.

[6]

C. Brunk, J. Kelly & R. Kohavi. MineSet: An integrated system for data mining. In Proc. KDD 1997, pp. 135--138.

[7]

S.-M. Chan, L. Xiao, J. Gerth & P. Hanrahan. Maintaining interactivity while exploring massive time series. In Proc. IEEE VAST 2008, pp. 59--66.

[8]

C.H. Chih & D.S. Parker. The persuasive phase of visualization. In Proc. KDD 2008, pp. 884--892.

Digital Library

[9]

J. Han, J. Pei, Y. Yin & R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1), pp. 53--87, 2004.

Digital Library

[10]

T. Iwata, T. Yamada & N. Ueda. Probabilistic latent semantic visualization: Topic model for visualizing documents. In Proc. KDD 2008, pp. 363--371.

Digital Library

[11]

D.A. Keim. Information visualization and visual data mining. IEEE TVCG, 8(1), pp. 1--8, 2002.

Digital Library

[12]

D.A. Keim & H.-P. Kriegel. Visualization techniques for mining large databases: A comparison. IEEE TKDE, 8(6), pp. 923--938, 1996.

Digital Library

[13]

D. Keim & J. Schneidewind (eds.) Special issue on visual analytics. SIGKDD Explorations, 9(2), 2007.

[14]

Y. Koren & D. Harel. A two-way visualization method for clustered data. In Proc. KDD 2003, pp. 589--594.

Digital Library

[15]

L.V.S. Lakshmanan, C.K.-S. Leung & R.T. Ng. Efficient dynamic mining of constrained frequent sets. ACM TODS, 28(4), pp. 337--389, 2003.

Digital Library

[16]

L.V.S. Lakshmanan, C.K.-S. Leung & R.T. Ng. The segment support map: Scalable mining of frequent itemsets. SIGKDD Explorations, 2(2), pp. 21--27, 2000.

Digital Library

[17]

H. Lam, D. Russell, D. Tang & T. Munzner. Session viewer: Visual exploratory analysis of web session logs. In Proc. IEEE VAST 2007, pp. 147--154.

Digital Library

[18]

C.K.-S. Leung. Frequent itemset mining with constraints. In Encyclopedia of Database Systems, pp. 1179--1183, Springer, 2009.

[19]

C.K.-S. Leung & C.L. Carmichael. FpViz: A visualizer for frequent pattern mining. In Proc. VAKD 2009, pp. 30--39.

Digital Library

[20]

C.K.-S. Leung & B. Hao. Mining of frequent itemsets from streams of uncertain data. In Proc. IEEE ICDE 2009, pp. 1663--1670.

Digital Library

[21]

C.K.-S. Leung, P.P. Irani & C.L. Carmichael. FIsViz: A frequent itemset visualizer. In Proc. PAKDD 2008, pp. 644--652.

Digital Library

[22]

C.K.-S. Leung, P.P. Irani & C.L. Carmichael. WiFIs-Viz: Effective visualization of frequent itemsets. In Proc. IEEE ICDM 2008, pp. 875--880.

Digital Library

[23]

C.K.-S. Leung, L.V.S. Lakshmanan & R.T. Ng. Exploiting succinct constraints using FP-trees. SIGKDD Explorations, 4(1), pp. 40--49, 2002.

Digital Library

[24]

C.K.-S. Leung, M.A.F.Mateo & D.A. Brajczuk. A treebased approach for frequent pattern mining from uncertain data. In Proc. PAKDD 2008, pp. 653--661.

Digital Library

[25]

C.K.-S. Leung, R.T. Ng & H. Mannila. OSSM: A segmentation approach to optimize frequency counting. In Proc. IEEE ICDE 2002, pp. 583--592.

Digital Library

[26]

T. Munzner, Q. Kong, R.T. Ng, J. Lee, J. Klawe, D. Radulovic & C.K.-S. Leung. Visual mining of power sets with large alphabets. Tech. rep. UBC CS TR-2005-25, Dept. Computer Science, UBC, Canada, 2005.

[27]

D. Oelke, P. Bak, D.A. Keim, M. Last & G. Danon. Visual evaluation of text features for document summarization and analysis. In Proc. IEEE VAST 2008, pp. 75--82.

[28]

N. Pasquier, Y. Bastide, R. Taouil & L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. ICDT 1999, pp. 398--416.

Digital Library

[29]

K. Puolamäki (ed.) Proc. VAKD 2009.

[30]

J. Scholtz. Beyond usability: Evaluation aspects of visual analytic environments. In Proc. IEEE VAST 2006, pp. 145--150.

[31]

T. Schreck, J. Bernard, T. Tekusova & J. Kohlhammer. Visual cluster analysis of trajectory data with interactive Kohonen Maps. In Proc. IEEE VAST 2008, pp. 3--10.

[32]

R. Spence. Information Visualization: Design for Interaction, 2e. Prentice Hall, 2007.

Digital Library

[33]

C. Stolte, D. Tang & P. Hanrahan. Query, analysis, and visualization of hierarchically structured data using Polaris. In Proc. KDD 2002, pp. 112--122.

Digital Library

[34]

C. Ware, H. Purchase, L. Colpoys & M. McGill. Cognitive measurements of graph aesthetics. Information Visualization, 1(2), pp. 103--110, 2002.

Digital Library

[35]

P.C. Wong & J. Thomas. Visual analytics. IEEE CG&A, 24(5), pp. 20--21, 2004.

Digital Library

[36]

L. Yang. Pruning and visualizing generalized association rules in parallel coordinates. IEEE TKDE, 17(1), pp. 60--70, 2005.

Digital Library

[37]

X. Yang, S. Asur, S. Parthasarathy & S. Mehta. A visual-analytic toolkit for dynamic interaction graphs. In Proc. KDD 2008, pp. 1016--1024.

Digital Library

[38]

J. Yuan, Y. Wu & M. Yang. From frequent itemsets to semantically meaningful visual patterns. In Proc. KDD 2007, pp. 864--873.

Digital Library

Cited By

M. AA. ARoy RSunil P(2023)Predictive big data analytics for drilling downhole problems: A reviewEnergy Reports10.1016/j.egyr.2023.05.0289(5863-5876)Online publication date: Dec-2023
https://doi.org/10.1016/j.egyr.2023.05.028
Hoi CLeung CPazdor A(2022)Data. Information and Knowledge Visualization for Frequent Patterns2022 26th International Conference Information Visualisation (IV)10.1109/IV56949.2022.00045(221-226)Online publication date: Jul-2022
https://doi.org/10.1109/IV56949.2022.00045
Smallwood JZhao CLeung CWen YZheng H(2022)Mining the Impacts of COVID-19 Pandemic on the Labour Market2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)10.1109/IMCOM53663.2022.9721772(1-8)Online publication date: 3-Jan-2022
https://doi.org/10.1109/IMCOM53663.2022.9721772
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FpVAT: a visual analytic tool for supporting frequent pattern mining
1. Information systems
  1. Information systems applications

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 11, Issue 2

December 2009

128 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1809400

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Copyright © 2010 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2010

Published in SIGKDD Volume 11, Issue 2

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https://doi.org/10.1016/j.egyr.2023.05.028
Hoi CLeung CPazdor A(2022)Data. Information and Knowledge Visualization for Frequent Patterns2022 26th International Conference Information Visualisation (IV)10.1109/IV56949.2022.00045(221-226)Online publication date: Jul-2022
https://doi.org/10.1109/IV56949.2022.00045
Smallwood JZhao CLeung CWen YZheng H(2022)Mining the Impacts of COVID-19 Pandemic on the Labour Market2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)10.1109/IMCOM53663.2022.9721772(1-8)Online publication date: 3-Jan-2022
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