| Computational complexity of itemset frequency satisfiability |
| Full text |
Pdf
(254 KB)
|
| Source
|
Symposium on Principles of Database Systems
archive
Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
table of contents
Paris, France
SESSION: Clustering, data mining, approximations
table of contents
Pages: 143 - 154
Year of Publication: 2004
ISBN:158113858X
|
|
Author
|
|
| Sponsors |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 3, Downloads (12 Months): 30, Citation Count: 8
|
|
|
ABSTRACT
Computing frequent itemsets is one of the most prominent problems in data mining. We introduce a new, related problem, called FREQSAT: given some itemset-interval pairs, does there exist a database such that for every pair the frequency of the itemset falls in the interval? It is shown in this paper that FREQSAT is not finitely axiomatizable and that it is NP-complete. We also study cases in which other characteristics of the database are given as well. These characteristics can complicate FREQSAT even more. For example, when the maximal number of duplicates of a transaction is known, FREQSAT becomes PP-hard. We describe applications of FREQSAT in frequent itemset mining algorithms and privacy in data mining.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
 |
1
|
Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
|
 |
2
|
|
 |
3
|
|
 |
4
|
|
| |
5
|
T. Calders, Deducing bounds on the support of itemsets. In Database Support for Data Mining Applications, LNCS 2682, Springer, to appear 2004.
|
| |
6
|
|
| |
7
|
V. Chvátal. Recognizing intersection patterns. Annals of Discrete Mathematics - Combinatorics 79, 8(I):249--251, 1980.
|
| |
8
|
|
| |
9
|
|
 |
10
|
|
 |
11
|
|
| |
12
|
H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proc. KDD Int. Conf. Knowledge Discovery in Databases, 1996.
|
| |
13
|
T. Mielikäinen. On inverse frequent set mining. In Workshop on Privacy Preserving Data Mining, 2003.
|
| |
14
|
|
| |
15
|
C. H. Papadimitriou. Computational Complexity. Addison-Wesley, 1994.
|
|