Name
Abstract | ||
|---|---|---|
The KDD process aims at searching for interesting instances of patterns in data sets. It is widely accepted that the patterns must be comprehensible. One of the aspects that are under-addressed in the KDD process is the handling of uncertainty in the process of clustering, classification and association rules extraction. In this paper we present a classification framework for relational databases so as to support uncertainty in terms of natural language queries and assessments. More specifically, we present a classification scheme of non-categorical attributes into lexically defined categories based on fuzzy logic and provides decision support facilities based on related information measures. I. INTRODUCTION |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/FUZZY.2000.838692 | Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference |
Keywords | DocType | Volume |
data mining,fuzzy logic,inference mechanisms,pattern classification,relational databases,uncertainty handling,classification,clustering,data mining,fuzzy logic,knowledge discovery,reasoning,relational databases,rules extraction,uncertainty handling | Conference | 1 |
ISSN | ISBN | Citations |
1098-7584 | 0-7803-5877-5 | 1 |
PageRank | References | Authors |
0.40 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michalis Vazirgiannis | 1 | 3942 | 268.00 |
| Maria Halkidi | 2 | 1304 | 72.90 |