Paper Info
Title
From Visualization to Association Rules: an automatic approach
Abstract
The main goal of Data Mining is the research of relevant information from a huge volume of data. It is generally achieved either by automatic algorithms or by the visual exploration of data. Thanks to algorithms, an exhaustive set of patterns matching specific measures can be found. But the volume of extracted information can be greater than the volume of initial data. Visual Data Mining allows the specialist to focus on a specific area of data that may describe interesting patterns. However, it is often limited by the difficulty to deal with a great number of multi dimensional data. In this paper, we propose to mix an automatic and a manual method, by driving the automatic extraction using a data scatter plot visualization. This visualization affects the number of rules found and their construction. We illustrate our method on two databases. The first describes one month French air traffic and the second stems from 2012 KDD Cup database.
Year
DOI
Venue
2013
10.1145/2508244.2508252
SCCG
Keywords
Field
DocType
great number,data scatter plot visualization,visual data mining,data mining,association rules,initial data,automatic extraction,multi dimensional data,manual method,huge volume,automatic approach,automatic algorithm
Multi dimensional data,Data mining,Information visualization,Computer science,Air traffic control,Visualization,Association rule learning,Artificial intelligence,Scatter plot,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Gwenael Bothorel151.57
Mathieu Serrurier226726.94
Christophe Hurter368447.30