skip to main content
10.1145/2020408.2020529acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
demonstration

MIME: a framework for interactive visual pattern mining

Published: 21 August 2011 Publication History

Abstract

We present a framework for interactive visual pattern mining. Our system enables the user to browse through the data and patterns easily and intuitively, using a toolbox consisting of interestingness measures, mining algorithms and post-processing algorithms to assist in identifying interesting patterns. By mining interactively, we enable the user to combine their subjective interestingness measure and background knowledge with a wide variety of objective measures to easily and quickly mine the most important and interesting patterns. Basically, we enable the user to become an essential part of the mining algorithm. Our demo currently applies to mining interesting itemsets and association rules, and its extension to episodes and decision trees is ongoing.

References

[1]
M. Ankerst, M. Ester, and H.-P. Kriegel. Towards an effective cooperation of the user and the computer for classification. In Proc. ACM SIGKDD, pages 179--188, 2000.
[2]
E. Bertini and D. Lalanne. Investigating and reflecting on the integration of automatic data analysis and visualization in knowledge discovery. SIGKDD Explor. Newsl., 11:9--18, May 2010.
[3]
H. Blockeel, T. Calders, E. Fromont, B. Goethals, A. Prado, and C. Robardet. An inductive database prototype based on virtual mining views. In Proc. ACM SIGKDD, pages 1061--1064, 2008.
[4]
C. L. Carmichael and C. K.-S. Leung. FpVAT: a visual analytic tool for supporting frequent pattern mining. SIGKDD Explor. Newsl., 11:39--48, May 2010.
[5]
A. Datta and K. Techapichetvanich. VisAR : a new technique for visualizing mined association rules. In X. Li, S. Wang, and Z. Dong, editors, Adv. Data Min. Appl., pages 728--728. Springer Berlin / Heidelberg, 2005.
[6]
T. De Bie. Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Disc., In press.
[7]
S. Dzeroski, B. Goethals, and P. Panov. Inductive Databases and Constraint-Based Data Mining. Springer-Verlag, 2010.
[8]
L. Geng and H. J. Hamilton. Interestingness measures for data mining: A survey. ACM Comput. Surv., 38, September 2006.
[9]
B. Goethals, N. Tatti, and J. Vreeken. Useful patterns (UP'10) ACM SIGKDD workshop report. SIGKDD Explor. Newsl., 12:56--58, March 2011.
[10]
D. A. Keim. Information visualization and visual data mining. IEEE Trans. Vis. Comp. Graph., 8:1--8, January 2002.
[11]
V. Kumar, J. Srivastava, and P.-N. Tan. Selecting the right interestingness measure for association patterns. In Proc. ACM SIGKDD, pages 32--41, 2002.

Cited By

View all
  • (2022)Fast Estimation of the Pattern Frequency SpectrumMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44851-9_8(114-129)Online publication date: 10-Mar-2022
  • (2021)Interactive Decision Tree Learning and Decision Rule Extraction Based on the ImbTreeEntropy and ImbTreeAUC PackagesProcesses10.3390/pr90711079:7(1107)Online publication date: 25-Jun-2021
  • (2021)SubSect—An Interactive Itemset VisualizationArtificial Intelligence and Machine Learning10.1007/978-3-030-65154-1_10(165-181)Online publication date: 5-Jan-2021
  • Show More Cited By

Index Terms

  1. MIME: a framework for interactive visual pattern mining

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2011
    1446 pages
    ISBN:9781450308137
    DOI:10.1145/2020408
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 August 2011

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. interactive visual mining
    2. mime
    3. pattern exploration

    Qualifiers

    • Demonstration

    Conference

    KDD '11
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Fast Estimation of the Pattern Frequency SpectrumMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44851-9_8(114-129)Online publication date: 10-Mar-2022
    • (2021)Interactive Decision Tree Learning and Decision Rule Extraction Based on the ImbTreeEntropy and ImbTreeAUC PackagesProcesses10.3390/pr90711079:7(1107)Online publication date: 25-Jun-2021
    • (2021)SubSect—An Interactive Itemset VisualizationArtificial Intelligence and Machine Learning10.1007/978-3-030-65154-1_10(165-181)Online publication date: 5-Jan-2021
    • (2020)Root-Cause Analysis with Interactive Decision Trees2020 24th International Conference Information Visualisation (IV)10.1109/IV51561.2020.00060(322-327)Online publication date: Sep-2020
    • (2020)A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event LogsNew Frontiers in Mining Complex Patterns10.1007/978-3-030-48861-1_1(3-20)Online publication date: 14-May-2020
    • (2019)A Smart Decision System for Digital FarmingAgronomy10.3390/agronomy90502169:5(216)Online publication date: 27-Apr-2019
    • (2019)Interactive evaluation of recommender systems with SNIPERProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346965(538-539)Online publication date: 10-Sep-2019
    • (2019)Obtaining Tractable and Interpretable Descriptions for Cases with Complications from a Colorectal Cancer Database2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2019.00095(459-464)Online publication date: Jun-2019
    • (2019)Method evaluation, parameterization, and result validation in unsupervised data mining: A critical surveyWIREs Data Mining and Knowledge Discovery10.1002/widm.133010:2Online publication date: 29-Jul-2019
    • (2018)Mining Redescriptions with SirenACM Transactions on Knowledge Discovery from Data10.1145/300721212:1(1-30)Online publication date: 31-Jan-2018
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media