Mining associations with the collective strength approach

IEEE Transactions on Knowledge and Data Engineering

Volumn 13, Issue 6, 2001, Pages 863-873

  Aggarwal, Charu C ^a   Yu, Philip S ^b  

^a IEEE   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Association rules; Data mining; Knowledge discovery

Indexed keywords

ASSOCIATION RULES;

ALGORITHMS; ASSOCIATIVE PROCESSING; COMPUTATIONAL METHODS; CORRELATION THEORY; DATABASE SYSTEMS; SET THEORY; STATISTICAL METHODS;

DATA MINING;

EID: 0035518001     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/69.971183     Document Type: Article

References (17)

1. A new framework for itemset generation
2. Mining association rules between sets of items in very large databases
3. Fast algorithms for mining association rules in large databases
4. From data mining to knowledge discovery: An overview
5. Beyond market baskets: Generalizing association rules to correlations
6. Dynamic itemset counting and implication rules for market basket data
7. Mining surprising patterns using temporal description length
8. Data mining: An overview from database perspective
9. Finding interesting rules from large sets of discovered association rules
10. Clustering association rules
11. Efficient algorithms for discovering association rules
12. Tests of CHI-squared type
13. An effective hash-based algorithm for mining association rules
14. On a criterion that a given system of deviations from the probable in the case of correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
15. Mining generalized association rules
16. Mining quantitative association rules in large relational tables