On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases
Summary :
Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.12 pp.2430-2438
- Publication Date
- 2009/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.2430
- Type of Manuscript
- PAPER
- Category
- Data Mining
Authors
Keyword
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Unil YUN, "On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 12, pp. 2430-2438, December 2009, doi: 10.1587/transinf.E92.D.2430.
Abstract: Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2430/_p
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@ARTICLE{e92-d_12_2430,
author={Unil YUN, },
journal={IEICE TRANSACTIONS on Information},
title={On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases},
year={2009},
volume={E92-D},
number={12},
pages={2430-2438},
abstract={Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.},
keywords={},
doi={10.1587/transinf.E92.D.2430},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases
T2 - IEICE TRANSACTIONS on Information
SP - 2430
EP - 2438
AU - Unil YUN
PY - 2009
DO - 10.1587/transinf.E92.D.2430
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2009
AB - Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.
ER -
English
