Name
Abstract | ||
|---|---|---|
Mining frequent partial orders from a collection of sequences was introduced as an alternative to mining frequent sequential patterns in order to provide a more compact/understandable representation. The motivation was that a single partial order can represent the same ordering information between items in the collection as a set of sequential patterns (set of totally ordered sets of items). However, in practice, a discovered set of frequent partial orders is still too large for an effective usage. We address this problem by proposing a method for ranking partial orders with respect to significance that extends our previous work on ranking sequential patterns. In experiments, conducted on a collection of visits to a website of a multinational technology and consulting firm we show the applicability of our framework to discover partial orders of frequently visited webpages that can be actionable in optimizing effectiveness of web-based marketing. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23780-5_49 | ECML/PKDD (1) |
Keywords | Field | DocType |
sequential pattern,actionable partial order,single partial order,ranking sequential pattern,multinational technology,frequent sequential pattern,effective usage,partial order,optimizing effectiveness,ranking partial order,frequent partial order | Data mining,Ordered set,Web page,Ranking,Computer science,Linear extension | Conference |
Volume | ISSN | Citations |
6911 | 0302-9743 | 3 |
PageRank | References | Authors |
0.44 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Gwadera | 1 | 124 | 13.81 |
| Gianluca Antonini | 2 | 192 | 13.67 |
| A Labbi | 3 | 20 | 4.43 |