Abstract
One of the goals of Association Mining is to develop algorithms capable of finding frequently co-occurring groups of items (itemsets) in transaction databases. The recently published technique of Itemset Trees expedited the processing of so-called targeted queries where the user is interested only in itemsets that contain certain prespecified items. However, the technique did not seem to offer any cost-effective way how to find all frequent itemsets (general queries) as it is common with other association-mining algorithms. The purpose of this paper is to rectify this deficiency by a newly developed algorithm that we call IT-Mining. Experimental results indicate that itemset trees can now with advantage be used to answer both targeted and general queries, and that the technique compares favorably with previous atttempts under a broad range of data parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 105-120 |
| Number of pages | 16 |
| Journal | Intelligent Data Analysis |
| Volume | 10 |
| Issue number | 2 |
| State | Published - Dec 1 2006 |
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Keywords
- Data mining
- frequent itemsets
- itemset trees
ASJC Scopus subject areas
- Artificial Intelligence
- Theoretical Computer Science
- Computer Vision and Pattern Recognition
Cite this
Searching for high-support itemsets in itemset trees. / Li, Yu; Kubat, Miroslav.
In: Intelligent Data Analysis, Vol. 10, No. 2, 01.12.2006, p. 105-120.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Searching for high-support itemsets in itemset trees
AU - Li, Yu
AU - Kubat, Miroslav
PY - 2006/12/1
Y1 - 2006/12/1
N2 - One of the goals of Association Mining is to develop algorithms capable of finding frequently co-occurring groups of items (itemsets) in transaction databases. The recently published technique of Itemset Trees expedited the processing of so-called targeted queries where the user is interested only in itemsets that contain certain prespecified items. However, the technique did not seem to offer any cost-effective way how to find all frequent itemsets (general queries) as it is common with other association-mining algorithms. The purpose of this paper is to rectify this deficiency by a newly developed algorithm that we call IT-Mining. Experimental results indicate that itemset trees can now with advantage be used to answer both targeted and general queries, and that the technique compares favorably with previous atttempts under a broad range of data parameters.
AB - One of the goals of Association Mining is to develop algorithms capable of finding frequently co-occurring groups of items (itemsets) in transaction databases. The recently published technique of Itemset Trees expedited the processing of so-called targeted queries where the user is interested only in itemsets that contain certain prespecified items. However, the technique did not seem to offer any cost-effective way how to find all frequent itemsets (general queries) as it is common with other association-mining algorithms. The purpose of this paper is to rectify this deficiency by a newly developed algorithm that we call IT-Mining. Experimental results indicate that itemset trees can now with advantage be used to answer both targeted and general queries, and that the technique compares favorably with previous atttempts under a broad range of data parameters.
KW - Data mining
KW - frequent itemsets
KW - itemset trees
UR - http://www.scopus.com/inward/record.url?scp=44349156339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44349156339&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:44349156339
VL - 10
SP - 105
EP - 120
JO - Intelligent Data Analysis
JF - Intelligent Data Analysis
SN - 1088-467X
IS - 2
ER -