The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'exploration de modèles corrélés dans de grandes bases de données de transactions est l'une des tâches essentielles de l'exploration de données, car un grand nombre de modèles sont généralement extraits, mais il est difficile de trouver des modèles avec corrélation. L'analyse des données nécessaire doit être effectuée en fonction des exigences de l'application réelle particulière. Dans les approches d'exploration de données précédentes, des modèles avec une faible affinité se trouvaient même avec un support minimum élevé. Dans cet article, nous suggérons une exploration de modèles d'affinité de support pondéré dans laquelle une nouvelle mesure, la confiance pondérée du support (ws-confidence), est développée pour identifier les modèles corrélés avec l'affinité de support pondérée. Pour élaguer efficacement les modèles de faible affinité, nous prouvons que la mesure de confiance ws satisfait les propriétés de support anti-monotone et à pondération croisée qui peuvent être appliquées pour éliminer les modèles avec des niveaux de support pondérés différents. Sur la base de ces deux propriétés, nous développons un algorithme d'exploration de modèles d'affinité de support pondéré (WSP). Les modèles d'affinité de support pondérés peuvent être utiles pour répondre aux requêtes d'analyse comparative telles que la recherche d'ensembles d'articles contenant des articles qui donnent des niveaux de frais de vente totaux similaires avec une plage d'erreur acceptable α % et la détection de listes d'articles avec des niveaux similaires de bénéfices totaux. De plus, notre étude de performance montre que WSP est efficace et évolutif pour les modèles d’affinité de support pondérés en matière d’exploitation minière.
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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},}
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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 -