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An Expert System for Problem Identification
최덕원,정차성 한국경영과학회 1996 한국경영과학회 학술대회논문집 Vol.- No.1
Managers are constantly facing problems. Some problems are treated with special connotation. Others are solved as a daily routine. While other problems disappear into the realm of oblivion without even recognized by managers. Some of the unrecognized or overlooked problems may cause a serious failure. It is also likely that there is a better solution approach even though we have been using a generally accepted method. Problem identification is a neglected area by researchers and managers, although they are facing problems everyday. This paper provides a review of the theories pertained to problem definition and problem identification as the beginning stage of the problem solving process. Based on these theories, we provide an expert system which can assist managers for a better problem solving is the key ingredient of the expert system.
Association Rule Discovery Considering Strategic Importance: WARM
최덕원,Choi, Doug-Won Korea Information Processing Society 2010 정보처리학회논문지D Vol.17 No.4
본 논문은 가중치를 고려한 연관규칙탐사 알고리즘(WARM)을 제시한다. 각 전략적 요소항목에 가중치를 부여하는 것과, 각 전략요소 항목별로 원시 자료값을 정규화하는 것이 이 논문에서 제시하는 알고리즘의 중요한 내용을 구성하고 있다. 본 논문은 TSAA 알고리즘을 확장 발전 시킨 연구로서 전략적 중요도를 반영하는 항목으로는 각 품목의 이익기여도, 마케팅 가치, 고객만족도 등을 사용하였다. 한 대형할인점의 실제 거래자료를 사용하여 알고리즘의 성능을 검사하였으며, Apriori, TSAA 및 WARM의 세 가지 알고리즘을 사용한 탐사결과를 비교 분석하였다. 분석의 결과 세 가지 알고리즘은 연관분석 행태에 있어서 각각 독특한 탐사행태를 보이는 것으로 나타났다. This paper presents a weight adjusted association rule mining algorithm (WARM). Assigning weights to each strategic factor and normalizing raw scores within each strategic factor are the key ideas of the presented algorithm. It is an extension of the earlier algorithm TSAA (transitive support association Apriori) and strategic importance is reflected by considering factors such as profit, marketing value, and customer satisfaction of each item. Performance analysis based on a real world database has been made and comparison of the mining outcomes obtained from three association rule mining algorithms (Apriori, TSAA, and WARM) is provided. The result indicates that each algorithm gives distinct and characteristic behavior in association rule mining.