VidaMine: a visual data mining environment

Abstract That the already vast and ever-increasing amounts of data still do present formidable challenges to effective and efficient acquisition of knowledge is by no means an exaggeration. The knowledge discovery process entails more than just the application of data mining strategies. There are many other aspects including, but not limited to: planning, data pre-processing, data integration, evaluation and presentation. The human-vision channel is capable of recognizing and understanding data at an instant. Effective visual strategies can be used to tap the outstanding human visual channel in extracting useful information from data. Unlike is the case with most research efforts, the exploitation should be employed not just at the beginning or at the end of the knowledge discovery process but across the entire discovery process. In essence, this calls for the development of an effective user/visual component, the development of an overall framework that can support the entire discovery process/all discovery phases, and the strategic placement of the visual component in that framework. Key issues of this component will be the open architecture, allowing extensions and adaptations to specific mining environments, and the precise semantics and syntax, allowing an optimal integration between the presentation and the computation.

[1]  Graham J. Wills,et al.  NicheWorks - Interactive Visualization of Very Large Graphs , 1997, GD.

[2]  Tiziana Catarci,et al.  Foundations of the DARE system for drawing adequate representations , 1999, Proceedings 1999 International Symposium on Database Applications in Non-Traditional Environments (DANTE'99) (Cat. No.PR00496).

[3]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[4]  Z. Meral Özsoyoglu,et al.  Indexing large metric spaces for similarity search queries , 1999, TODS.

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

[6]  Christopher A. Badurek,et al.  Review of Information visualization in data mining and knowledge discovery by Usama Fayyad, Georges G. Grinstein, and Andreas Wierse. Morgan Kaufmann 2002 , 2003 .

[7]  B. Jaumard,et al.  Cluster Analysis and Mathematical Programming , 2003 .

[8]  G. Krishna,et al.  Agglomerative clustering using the concept of mutual nearest neighbourhood , 1978, Pattern Recognit..

[9]  Luigi Palopoli,et al.  Computational properties of metaquerying problems , 2000, PODS '00.

[10]  Download Book,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[11]  Tiziana Catarci,et al.  The prototype of the DARE system , 2001, SIGMOD '01.

[12]  Carlo Zaniolo,et al.  Metaqueries for Data Mining , 1996, Advances in Knowledge Discovery and Data Mining.

[13]  Pak Chung Wong,et al.  Guest Editor's Introduction: Visual Data Mining , 1999, IEEE Computer Graphics and Applications.

[14]  Jakob Nielsen,et al.  Heuristic evaluation of user interfaces , 1990, CHI '90.

[15]  Tiziana Catarci,et al.  What Happened When Database Researchers Met Usability , 2000, Inf. Syst..

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

[17]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[18]  Tiziana Catarci,et al.  DARE: a multidimensional environment for visualizing large set of medical data , 2002, Proceedings Sixth International Conference on Information Visualisation.

[19]  Ronald J. Brachman,et al.  Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud , 2004, Data Mining and Knowledge Discovery.

[20]  Ray A. Jarvis,et al.  Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.

[21]  Graham J. Wills.  NicheWorks—Interactive Visualization of Very Large Graphs , 1999 .

[22]  Jiawei Han,et al.  DBMiner: A System for Mining Knowledge in Large Relational Databases , 1996, KDD.