Name
Abstract | ||
|---|---|---|
A hierarchical approach is natural when managing large volumes of information, from both static (database) and dynamic (datastream) sources. Hierarchies allow progressively finer division into more specific categories, but frequently the categories are fuzzy rather than crisp. In this paper, we use fuzzy formal concept analysis to extract soft hierarchies from data. The hierarchies are used to classify data and to monitor changes over time by means of a fuzzy confidence measure for association analysis. A (simulated) stream of terrorism incident data is used as proof of concept. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14058-7_5 | Communications in Computer and Information Science |
Keywords | Field | DocType |
fuzzy formal concept hierarchies,fuzzy association rules,fuzzy confidence,dynamic data streams | Data mining,Data stream mining,Fuzzy classification,Fuzzy formal concept analysis,Fuzzy set operations,Fuzzy logic,Proof of concept,Fuzzy association rules,Hierarchy,Mathematics | Conference |
Volume | ISSN | Citations |
81 | 1865-0929 | 1 |
PageRank | References | Authors |
0.37 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trevor P. Martin | 1 | 134 | 26.98 |
| Yun Shen | 2 | 185 | 18.88 |
| Andrei Majidian | 3 | 25 | 2.98 |