Abstract
The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (itemsets) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the list of frequent itemsets may change in time as the purchasing habits get affected by fashion, season, and introduction of new products. We investigate (1) heuristics for the detection of such changes in time-ordered databases and (2) techniques that update the set of frequent itemsets when the change is detected. As the main performance criterion, we use the accuracy with which our program maintains the current list of frequent itemsets in a time-varying environment.
| Original language | English |
|---|---|
| Pages (from-to) | 273-288 |
| Number of pages | 16 |
| Journal | Intelligent Data Analysis |
| Volume | 9 |
| Issue number | 3 |
| State | Published - Dec 1 2005 |
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Keywords
- association mining
- change detection
- similarity metrics
ASJC Scopus subject areas
- Artificial Intelligence
- Theoretical Computer Science
- Computer Vision and Pattern Recognition
Cite this
Association mining in time-varying domains. / Rozsypal, Antonin; Kubat, Miroslav.
In: Intelligent Data Analysis, Vol. 9, No. 3, 01.12.2005, p. 273-288.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Association mining in time-varying domains
AU - Rozsypal, Antonin
AU - Kubat, Miroslav
PY - 2005/12/1
Y1 - 2005/12/1
N2 - The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (itemsets) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the list of frequent itemsets may change in time as the purchasing habits get affected by fashion, season, and introduction of new products. We investigate (1) heuristics for the detection of such changes in time-ordered databases and (2) techniques that update the set of frequent itemsets when the change is detected. As the main performance criterion, we use the accuracy with which our program maintains the current list of frequent itemsets in a time-varying environment.
AB - The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (itemsets) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the list of frequent itemsets may change in time as the purchasing habits get affected by fashion, season, and introduction of new products. We investigate (1) heuristics for the detection of such changes in time-ordered databases and (2) techniques that update the set of frequent itemsets when the change is detected. As the main performance criterion, we use the accuracy with which our program maintains the current list of frequent itemsets in a time-varying environment.
KW - association mining
KW - change detection
KW - similarity metrics
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UR - http://www.scopus.com/inward/citedby.url?scp=67749084610&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:67749084610
VL - 9
SP - 273
EP - 288
JO - Intelligent Data Analysis
JF - Intelligent Data Analysis
SN - 1088-467X
IS - 3
ER -