A Visualization Model of Interactive Knowledge Discovery Systems and Its Implementations

We briefly introduce an interactive visualization model, RuleViz, for knowledge discovery and data mining, which consists of five components: data preparation and visualization, interactive data reduction, data preprocessing, pattern discovery, and pattern visualization. With this model, the implementation issues are considered and three implementation paradigms, including image-based paradigm, algorithm-embedded paradigm, and interaction-driven paradigm, are discussed. We implement an interactive visualization system, AViz, which discovers 3D numerical association rules from large data sets based on the image-based paradigm. The framework of the AViz system is presented and each component is explored. To discretize numerical attributes, three approaches, including equal-sized, bin-packing-based equal-depth, and interaction-based approaches are proposed, and the algorithm for mining and visualizing numerical association rules is developed. Our experimental result on a census data set is illustrated, which shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.

[1]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[2]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[3]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[4]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[5]  Hans-Peter Kriegel,et al.  Visualization Techniques for Mining Large Databases: A Comparison , 1996, IEEE Trans. Knowl. Data Eng..

[6]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[7]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[8]  Ronald J. Brachman,et al.  The Process of Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[9]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[10]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[11]  Nick Cercone,et al.  ELEM2: A Learning System for More Accurate Classifications , 1998, Canadian Conference on AI.

[12]  Nick Cercone,et al.  RuleViz: a model for visualizing knowledge discovery process , 2000, KDD '00.

[13]  Hans-Peter Kriegel,et al.  Towards an Effective Cooperation of the Computer and the User for Classification , 2000, KDD 2000.

[14]  Stefan Berchtold,et al.  Independence Diagrams: A Technique for Visual Data Mining , 1998, KDD.

[15]  Yasuhiko Morimoto,et al.  Mining optimized association rules for numeric attributes , 1996, J. Comput. Syst. Sci..

[16]  Nick Cercone,et al.  Visualizing the process of knowledge discovery , 2000, J. Electronic Imaging.

[17]  Nick Cercone,et al.  AViz: A Visualization System for Discovering Numeric Association Rules , 2000, PAKDD.

[18]  Jock D. Mackinlay,et al.  Information visualization using 3D interactive animation , 1993, CACM.

[19]  Steven F. Roth,et al.  An Interactive Visualization Environment for Data Exploration , 1997, KDD.

[20]  Pieter Adriaans,et al.  Data mining , 1996 .

[21]  Ron Kohavi,et al.  MineSet: An Integrated System for Data Mining , 1997, KDD.

[22]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[23]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[24]  Nick Cercone,et al.  Interactive Construction of Decision Trees , 2001, PAKDD.

[25]  Chen Li.  Knowledge Discovery in Database , 1999 .

[26]  Nick Cercone,et al.  CViz: An Interactive Visualization System for Rule Induction , 2000, Canadian Conference on AI.

[27]  John Adkins Richardson,et al.  Visual Explanations: Images and Quantities, Evidence and Narrative , 1999 .

[28]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[29]  Heike Hofmann,et al.  Visualizing association rules with interactive mosaic plots , 2000, KDD '00.

[30]  Lisa Tweedie,et al.  Characterizing interactive externalizations , 1997, CHI.

[31]  George W. Furnas,et al.  Effective view navigation , 1997, CHI.

[32]  Christopher Ahlberg,et al.  Spotfire: an information exploration environment , 1996, SGMD.

[33]  Hans-Peter Kriegel,et al.  Visual classification: an interactive approach to decision tree construction , 1999, KDD '99.

[34]  John V. Carlis,et al.  Interactive visualization of serial periodic data , 1998, UIST '98.

[35]  Robert Groth,et al.  Data mining - a hands-on approach for business professionals , 1997 .

[36]  Edward R. Tufte.  Visual explanations: images and quantities, evidence and narrative , 1997 .

[37]  Charu C. Aggarwal,et al.  Towards effective and interpretable data mining by visual interaction , 2002, SKDD.

[38]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[39]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[40]  Renée J. Miller,et al.  Association rules over interval data , 1997, SIGMOD '97.

[41]  Yasuhiko Morimoto,et al.  Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization , 1996, SIGMOD '96.