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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
장바구니 분석에서는 WAR(Weighted Association Rule) 발견을 통해 특정 상품에 대한 품목 중요도를 반영하여 보다 유익한 정보를 포함하는 규칙을 마이닝할 수 있습니다. POS 데이터베이스에서 각 거래는 유사한 속성을 가진 품목으로 구성되며 품목 가중치는 이익과 같은 요소에 의해 사전 정의되고 고정됩니다. 그러나 항목이 둘 이상의 그룹으로 나누어지고 항목 중요도를 각 그룹에 대해 독립적으로 측정해야 하는 경우 기존의 가중치 연관 규칙 검색을 사용할 수 없습니다. 이 문제를 해결하기 위해 우리는 새로운 가중치 연관 규칙 마이닝 방법론을 제안합니다. 먼저 항목을 속성에 따라 하위 그룹으로 나누고 하위 그룹에 포함된 항목만으로 항목 중요도, 즉 항목 가중치를 정의하거나 계산합니다. 그런 다음, 각 하위 그룹의 품목 가중치를 적절하게 합산하여 거래 가중치를 측정하고, 가중치 지지도는 전체 거래 가중치 대비 후보 품목이 포함된 거래 가중치의 비율로 계산됩니다. 예를 들어, 제안된 방법론은 네트워크 서비스를 제공하는 컴퓨터 시스템의 위협에 대한 취약성을 평가하는 데 적용됩니다. 우리의 알고리즘은 WAR 검색을 사용하여 네트워크로 연결된 컴퓨터 시스템의 보안 평가를 위한 정량적 위험 수준 값과 정성적 위험 규칙을 모두 제공합니다. 또한, 데이터 항목이 뚜렷하게 구분되는 많은 데이터 세트가 있는 새로운 응용 프로그램에 널리 사용될 수 있습니다.
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부
Jungja KIM, Heetaek CEONG, Yonggwan WON, "Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 1, pp. 10-15, January 2009, doi: 10.1587/transinf.E92.D.10.
Abstract: In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.10/_p
부
@ARTICLE{e92-d_1_10,
author={Jungja KIM, Heetaek CEONG, Yonggwan WON, },
journal={IEICE TRANSACTIONS on Information},
title={Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems},
year={2009},
volume={E92-D},
number={1},
pages={10-15},
abstract={In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.},
keywords={},
doi={10.1587/transinf.E92.D.10},
ISSN={1745-1361},
month={January},}
부
TY - JOUR
TI - Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems
T2 - IEICE TRANSACTIONS on Information
SP - 10
EP - 15
AU - Jungja KIM
AU - Heetaek CEONG
AU - Yonggwan WON
PY - 2009
DO - 10.1587/transinf.E92.D.10
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2009
AB - In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.
ER -