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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dc.contributor.advisorPark, Sang-Chan-
dc.contributor.advisor박상찬-
dc.contributor.authorPark, Jae-Heon-
dc.contributor.author박재헌-
dc.date.accessioned2011-12-14T02:39:49Z-
dc.date.available2011-12-14T02:39:49Z-
dc.date.issued2003-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=181079&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/40553-
dc.description학위논문(박사) - 한국과학기술원 : 산업공학과, 2003.2, [ vii, 128 p. ]-
dc.description.abstractBy 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...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectData Mining-
dc.subjectKnowledge Discovery in Databases-
dc.subjectMixed Feature-
dc.subject고개 관계 관리-
dc.subjectFeature Weighting-
dc.subjectCustomer Relationship Management-
dc.subject지식 추출-
dc.subject데이타 마이닝-
dc.subject혼합 자료-
dc.subject가중치법-
dc.titleKnowledge 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-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN181079/325007-
dc.description.department한국과학기술원 : 산업공학과, -
dc.identifier.uid020005132-
dc.contributor.localauthorPark, Sang-Chan-
dc.contributor.localauthor박상찬-
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