Name
Abstract | ||
|---|---|---|
This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional
association rule, this combined association rule allows users to perform actions directly. Combined association rules are
always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules
consist of non-actionable attributes, actionable attributes, and class attribute, with the rules in one set sharing the same
non-actionable attributes. Thus, for a group of objects having the same non-actionable attributes, the actions corresponding
to a preferred class can be performed directly. However, standard association rule mining algorithms encounter many difficulties
when applied to combined association rule mining, and hence new algorithms have to be developed for combined association rule
mining. In this paper, we will focus on rule generation and interestingness measures in combined association rule mining.
In rule generation, the frequent itemsets are discovered among itemset groups to improve efficiency. New interestingness measures
are defined to discover more actionable knowledge. In the case study, the proposed algorithm is applied into the field of
social security. The combined association rule provides much greater actionable knowledge to business owners and users.
|
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68125-0_115 | Pacific-Asia Conference on Knowledge Discovery and Data Mining |
Keywords | Field | DocType |
single combined association rule,combined association rule,novel association rule,rule generation,single rule,rule set,combined association rule mining,non-actionable attribute,standard association rule mining,conventional association rule,association rule,association rule mining | Data mining,Computer science,Association rule learning,Artificial intelligence,Machine learning,K-optimal pattern discovery | Conference |
Volume | ISSN | ISBN |
5012 | 0302-9743 | 3-540-68124-8 |
Citations | PageRank | References |
10 | 0.59 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaifeng Zhang | 1 | 240 | 18.84 |
| Yanchang Zhao | 2 | 233 | 20.01 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Chengqi Zhang | 4 | 3636 | 274.41 |