|
Activity Number:
|
461
|
|
Type:
|
Invited
|
|
Date/Time:
|
Thursday, August 7, 2008 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
IMS
|
| Abstract - #300142 |
|
Title:
|
Symbolic Data Examples, Analytic Aspects, and SODAS Software
|
|
Author(s):
|
Edwin Diday*+
|
|
Companies:
|
Paris Dauphine University
|
|
Address:
|
PL; Mle De Lattre de Tassigny, Paris, 75016, France
|
|
Keywords:
|
Data Mining, ; conceptual statistics ; Symbolic Data Analysis, ; Knowledge mining,
|
|
Abstract:
|
Databases are now ubiquitous in industrial companies and public administrations and they often grow to an enormous size. They contain units described by variables that are often categorical or numerical (which can be also transformed to categories). It is then easy to build categories or Cartesian product of categories or categories by using a clustering process which yields to clusters defining each category. Symbolic data represented by structured variables, intervals, list, histograms, distributions, curves and the like, keep the "internal variation" of categories better than do standard data. The aim of Symbolic Data Analysis is to generalize Data Mining and Statistics to higher-level units called "concepts" (which represents these categories) described by symbolic data. The SODAS software (sponsored by EUROSTAT) extends standard tools of Statistics and Data Mining to these units.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2008 program |
