Knowledge discovery based on enhanced feature handling methods - mixed features and local feature weighting - and their application to CRM = 일반적인 형태의 자료 처리 및 질의 특성에 따른 가중치법의 지능적인 자료 처리 방법에 기반한 지식 추출 과정 및 고객관계관리에의 적용

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By data flood produced by automation of business activities and rapidly changed business environment, knowledge discovery in databases (KDD), namely methodologies for extraction useful knowledge from database, came to play a very important role in business. Especially, data mining, the heart of the KDD process, has been taking a lot of attentions and many researchers have been trying to develop efficient data mining methodologies or algorithms. Though a lot of algorithms have been developed, there remain many problems unsolved. Among them, we focused on feature handling area, especially feature weighting and mixed feature problems. We developed three algorithms-MBNR, k-representatives algorithm, and k-GR algorithm, for the problems. The performance of each algorithm is proved by datasets from UCI Machine Learning Repository. MBNR (Memory-Based Neural Reasoning) is a hybrid system of Case-Based Reasoning (CBR) and Neural Network. CBR has a very simple and comprehensible reasoning process but its prediction accuracy is a little low. In contrast to CBR, Neural Network shows very accurate prediction ability in many areas but it takes black-box approach, which means that it doesn``t provide comprehensible knowledge to users. The basic reasoning process of MBNR is that of CBR. The integrated Neural Network provides feature weights according a query coming to the system. The proposed hybrid system takes strengths from both CBR and Neural Network and provides very accurate and comprehensible prediction results. k-representatives algorithm is a efficient algorithm for clustering nominal data. Most of previous clustering algorithms for nominal data use the number of mismatching nominal features as the difference measure. They don``t take the similarity between values into account and only consider they are same or not. k-representatives algorithm provides a new iterative refinement clustering approach with consideration of the similarity between nominal values. k-GR (Gen...
Park, Sang-Chan박상찬
한국과학기술원 : 산업공학과,
Issue Date
181079/325007 / 020005132

학위논문(박사) - 한국과학기술원 : 산업공학과, 2003.2, [ vii, 128 p. ]


Data Mining; Knowledge Discovery in Databases; Mixed Feature; 고개 관계 관리; Feature Weighting; Customer Relationship Management; 지식 추출; 데이타 마이닝; 혼합 자료; 가중치법

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