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
대용량 트랜잭션 데이터베이스에서 상관 패턴을 마이닝하는 것은 데이터 마이닝의 필수 작업 중 하나입니다. 일반적으로 수많은 패턴을 마이닝하지만 상관 관계가 있는 패턴을 찾기가 어렵습니다. 필요한 데이터 분석은 특정 실제 애플리케이션의 요구 사항에 따라 이루어져야 합니다. 이전 마이닝 접근 방식에서는 최소 지지도가 높더라도 친화력이 약한 패턴이 발견되었습니다. 본 논문에서는 가중치 지원 선호도와 상관 관계가 있는 패턴을 식별하기 위해 새로운 측정값인 가중치 지원 신뢰도(ws-confidence)를 개발하는 가중치 지원 선호도 패턴 마이닝을 제안합니다. 약한 친화도 패턴을 효율적으로 제거하기 위해 ws-신뢰도 측정값이 가중치 지원 수준이 서로 다른 패턴을 제거하는 데 적용할 수 있는 반단조 및 교차 가중 지원 속성을 충족한다는 것을 증명합니다. 두 가지 속성을 기반으로 가중치 지원 선호도 패턴 마이닝 알고리즘(WSP)을 개발합니다. 가중 지지 선호도 패턴은 허용 가능한 오류 범위 α%로 유사한 총 판매 비용 수준을 제공하는 항목이 포함된 항목 집합을 찾고, 총 이익 수준이 비슷한 항목 목록을 검색하는 등 비교 분석 쿼리에 답하는 데 유용할 수 있습니다. 또한 성능 연구에 따르면 WSP는 가중치 기반 지원 선호도 패턴 마이닝에 효율적이고 확장 가능합니다.
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부
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
부
@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},}
부
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 -