Mining Actionable Partial Orders in Collections of Sequences
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.
Cite
Text
Gwadera et al. "Mining Actionable Partial Orders in Collections of Sequences." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23780-5_49Markdown
[Gwadera et al. "Mining Actionable Partial Orders in Collections of Sequences." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/gwadera2011ecmlpkdd-mining/) doi:10.1007/978-3-642-23780-5_49BibTeX
@inproceedings{gwadera2011ecmlpkdd-mining,
title = {{Mining Actionable Partial Orders in Collections of Sequences}},
author = {Gwadera, Robert and Antonini, Gianluca and Labbi, Abderrahim},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {613-628},
doi = {10.1007/978-3-642-23780-5_49},
url = {https://mlanthology.org/ecmlpkdd/2011/gwadera2011ecmlpkdd-mining/}
}