Abstract
In this paper, we describe a novel technique, called APACS2, for mining interesting quantitative association rules from very large databases. To effectively mine such rules. APACS2 employs adjusted difference analysis. The use of this technique has the advantage that it does not require any user- supplied thresholds which are often hard to determine. Furthermore, APACS2 also has the advantage that it allows users to discover both positive and negative association rules. A positive association rule tells us that a record having certain attribute value will also have another attribute value whereas a negative association rule tells us that a record having certain attribute value will not have another attribute value. The fact that APACS2 is able to mine both positive and negative association rules and that it uses an objective yet meaningful measure to determine the interestingness of a rule makes it very effective at different data mining tasks.
| Original language | English |
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| Title of host publication | Proceedings of the 1997 ACM Symposium on Applied Computing, SAC 1997 |
| Publisher | Association for Computing Machinery |
| Pages | 88-90 |
| Number of pages | 3 |
| ISBN (Print) | 0897918509, 9780897918503 |
| DOIs | |
| Publication status | Published - 1 Jan 1997 |
| Event | 1997 ACM Symposium on Applied Computing, SAC 1997 - San Jose, CA, United States Duration: 28 Feb 1997 → 1 Mar 1997 |
Conference
| Conference | 1997 ACM Symposium on Applied Computing, SAC 1997 |
|---|---|
| Country/Territory | United States |
| City | San Jose, CA |
| Period | 28/02/97 → 1/03/97 |
Keywords
- Data mining
- Interestingness measure
- Negative association rules
- Positive association rules
- Quantitative association rules
ASJC Scopus subject areas
- Software