Paper Info
Title
JRV: an interactive tool for data mining visualization
Abstract
In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.
Year
DOI
Venue
2004
10.1145/986537.986649
ACM Southeast Regional Conference 2005
Keywords
Field
DocType
general model,knowledge discovery process,novel attribute relationship,data visualization,multiple attribute,visualization tool,user interactively,domain knowledge,new data,efficient data visualization,interactive tool,data mining visualization,data mining,information visualization,design,pattern recognition,interactive visualization,algorithms,indexation,evaluation function
Data mining,Data visualization,Data stream mining,Domain knowledge,Information visualization,Computer science,Visualization,Visual analytics,Interactive visualization,Knowledge extraction
Conference
ISBN
Citations 
PageRank 
1-58113-870-9
1
0.35
References 
Authors
10
3
Name
Order
Citations
PageRank
Danyu Liu129819.96
Alan Sprague237245.53
Upender Manne310.35