Name
Abstract | ||
|---|---|---|
Tree structures are a natural way of describing occurrence relationships between attributes in a dataset. We define a new class of tree patterns for unordered 0–1 data and consider the problem of discovering frequently occurring members of this pattern class. Intuitively, a tree T occurs in a row u of the data, if the attributes of T that occur in u form a subtree of T containing the root. We show that this definition has advantageous properties: only shallow trees have a significant probability of occurring in random data, and the definition allows a simple levelwise algorithm for mining all frequently occurring trees. We demonstrate with empirical results that the method is feasible and that it discovers interesting trees in real data. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11871637_20 | PKDD |
Keywords | Field | DocType |
u form,tree structure,row u,shallow tree,random data,new class,tree pattern,interesting tree,pattern class | Data mining,Computer science,Tree (data structure),Association rule learning,Tree structure,Knowledge extraction,Tree pattern | Conference |
Volume | ISSN | ISBN |
4213 | 0302-9743 | 3-540-45374-1 |
Citations | PageRank | References |
1 | 0.39 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hannes Heikinheimo | 1 | 55 | 3.54 |
| Heikki Mannila | 2 | 6595 | 1495.69 |
| Jouni K. Seppänen | 3 | 124 | 9.09 |