Guiding knowledge discovery through interactive data mining

Knowledge discovery is the process of eliciting interesting knowledge from data repositories. Due to the inability of computers to understand abstract concepts, present mining algorithms do not adequately constrain the generation of rules to those that are of interest to the user. Interactive mining techniques aim to alleviate this problem by involving the user in the mining process, so that the user's understanding of abstract semantic concepts and domain knowledge can guide the discovery process, resulting in accelerated mining with improved results. This chapter presents a discussion of the current state of interactive data mining research.

[1]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[2]  Daniel A. Keim,et al.  Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering , 1999, VLDB.

[3]  W. R. Garner,et al.  Operationism and the concept of perception. , 1956, Psychological review.

[4]  Heikki Mannila,et al.  A data mining methodology and its application to semi-automatic knowledge acquisition , 1997, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings.

[5]  Daniel A. Keim,et al.  HD-Eye: Visual Mining of High-Dimensional Data , 1999, IEEE Computer Graphics and Applications.

[6]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[7]  Jock D. Mackinlay,et al.  The perspective wall: detail and context smoothly integrated , 1991, CHI.

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

[9]  Graham J. Wills,et al.  An interactive view for hierarchical clustering , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[10]  Paul Hudak,et al.  A theory of incremental computation and its application , 1991, POPL '91.

[11]  Anthony K. H. Tung,et al.  Spatial clustering in the presence of obstacles , 2001, Proceedings 17th International Conference on Data Engineering.

[12]  William Ribarsky,et al.  Discovery Visualization Using Fast Clustering , 1999, IEEE Computer Graphics and Applications.

[13]  Daniel Asimov,et al.  The grand tour: a tool for viewing multidimensional data , 1985 .

[14]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[15]  Markus Gross,et al.  H-BLOB: a hierarchical visual clustering method using implicit surfaces , 2000 .

[16]  E. T. Klemmer,et al.  Assimilation of information from dot and matrix patterns. , 1952, Journal of experimental psychology.

[17]  Daniel B. Carr,et al.  Scatterplot matrix techniques for large N , 1986 .

[18]  Margaret H. Dunham,et al.  Interactive Clustering for Transaction Data , 2001, DaWaK.

[19]  D. Cook,et al.  Interactive visualization of hierarchical clusters using MDS and MST , 2000 .

[20]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[21]  Manojit Sarkar,et al.  Graphical fisheye views , 1994, CACM.

[22]  Pak Chung Wong,et al.  Visualizing sequential patterns for text mining , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[23]  Arne Frick,et al.  Fast Interactive 3-D Graph Visualization , 1995, GD.

[24]  William Buxton,et al.  Chunking and Phrasing and the Design of Human-Computer Dialogues (Invited Paper) , 1995, IFIP Congress.

[25]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[26]  Markus Gross,et al.  Visualizing Informationon a Sphere , 1997 .

[27]  G. W. Furnas,et al.  Generalized fisheye views , 1986, CHI '86.

[28]  Daniel A. Keim,et al.  Clustering methods for large databases: from the past to the future , 1999, SIGMOD '99.

[29]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[30]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[31]  Forrest W. Young.  Multidimensional Scaling: History, Theory, and Applications , 1987 .

[32]  Christos Faloutsos,et al.  FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets , 1995, SIGMOD '95.

[33]  David J. DeWitt,et al.  Using a knowledge cache for interactive discovery of association rules , 1999, KDD '99.

[34]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[35]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[36]  Allen Newell,et al.  A theory of stimulus-response compatibility applied to human-computer interaction , 1985, CHI '85.

[37]  M. Sheelagh T. Carpendale,et al.  Extending Distortion Viewing from 2D to 3D , 1997, IEEE Computer Graphics and Applications.

[38]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[39]  John F. Roddick,et al.  Visualisation of Temporal Interval Association Rules , 2000, IDEAL.

[40]  Aidong Zhang,et al.  WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases , 1998, VLDB.

[41]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.

[42]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[43]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[44]  Andreas Buja,et al.  Grand tour and projection pursuit , 1995 .

[45]  Man Hon Wong,et al.  Interactive data analysis on numeric-data , 1999, Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265).

[46]  Ken Perlin,et al.  Human-guided simple search: combining information visualization and heuristic search , 1999, NPIVM '99.

[47]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[48]  Li Yang,et al.  n23tool: a tool for exploring large relational datasets through 3D dynamic projections , 2000, CIKM '00.

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