• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.569-574
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• 2011

In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.195-212
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• 2022

Based on the kinetic typography principle and the structure features of Chinese characters, this study took the Chinese'24 solar terms' theme dynamic poster as the research object, explored the visual expression of dynamic Chinese characters, and tried to summarize the visual expression law of Chinese characters in dynamic poster design. It can be found that, there could be 6 different types of Chinese character expressions in the 24 solar terms poster design. Among them, 'Drawing' design method has the meaning of text structure and form expression, and 'Assembling' design method has the meaning of text stroke and texture association, also, 'Forming' design method bring its meaning through stroke deformation, 'Transforming' design method conveys the content through text disintegration, 'Replacing' design method mainly bring the meaning through simulation, while 'Rotation' design method always express through visual three-dimensional and space. Finally, the findings could not only provide analytical logic and methods for the expression of Chinese characters in dynamic poster design, but also fill the lack of formative research on dynamic Chinese characters, which hopefully provide basic information for the research related to dynamic Chinese character structure, as well as the dynamic poster designers.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1017-1024
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• 2004

Ubiquitous system requires intelligent environment and system that perceives context in a proactive manner. This paper describes proactive retrieval method using context patterns in ubiquitous computing. And as the user's contexts change, new information is delivered proactively based on user's context patterns. For proactive retrieval, we extract context patterns based on sequential pattern discovery and association rule in data mining. By storing only information to be needed in near future using the context patterns, we solved the problem of speed and storage capacity of mobile devices in ubiquitous computing. We explain algorithms and an example. Several experiments are performed and the experimental results show that our method has a good information retrieval.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.19-25
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• 2020

In frequent pattern mining, the uncertainty of each item is accompanied by a loss of information. AAlso, in real environment, the importance of patterns changes with time, so fuzzy logic must be applied to meet these requirements and the dynamic characteristics of the importance of patterns should be considered. In this paper, we propose a fuzzy utility mining technique for extracting frequent web page sets from web log databases through fuzzy utility-based web page set mining. Here, the downward closure characteristic of the fuzzy set is applied to remove a large space by the minimum fuzzy utility threshold (MFUT)and the user-defined percentile(UDP). Extensive performance analyses show that our algorithm is very efficient and scalable for Fuzzy Utility Mining using dynamic weights.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.249-258
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• 2004

오늘날 활발하게 이루어지고 있는 유비쿼터스 컴퓨팅 관련 기술 연구는 사용자가 시간과 장소에 구애받지 않고 네트워크에 접근해 다양한 컴퓨터 관련 서비스를 제공 받을 수 있는 방법에 초점을 맞추고 있다. 이 처럼 시간과 공간의 한계를 뛰어 넘은 네트워크로의 자유로운 접근은 일상 생활의 패러다임을 바꾸어 놓게 될 것이다. 유비쿼터스 컴퓨팅 기술을 통해 가장 큰 변화가 일어나는 분야는 일반 가정환경에서 일어나는 인텔리전트 홈 네트워크 (Intelligent Home Network) 라고 할 수 있다. 집에 들어오면, 자동으로 문을 열어주고, 불을 켜주며, 놓쳤던 TV 프로그램을 자동으로 녹화해 놓았다가 원하는 시간에 보여주고, 적당한 시간에 목욕물을 미리 받아준다. 또한 집밖으로 나가기 전, 일기예보에 따라 우산을 챙겨주고, 일정을 확인시켜주며 입고 나갈 옷을 골라줄 수도 있다. 이 모든 일들이 유비쿼터스 컴퓨팅 기술이 가져올 인텔리전트 홈 네트워크의 모습이다. 그러나, 모든 사용자에게 효과적인 서비스를 제공하기 위해서는 홈 네트워크 상의 자원 관리에서 일어날 수 있는 에이전트들간의 자원 접근 권한 충돌을 효율적으로 방지할 수 있는 기술이 필요하다. 유비쿼터스 컴퓨팅 환경에서 자원관리 특성은 점유의 연속성, 자원 사이의 연관성, 그리고 자원과 사용자 사 사이의 연계성의 3 가지 특성을 지니고 있다. 본 논문에서는 유비쿼터스 컴퓨팅 환경에서 일어날 수 있는 자원 충돌 상황을 효율적으로 처리하기 위한 자원 협상 방법을 제안한다. 본 방법은 자원 관리 특성을 바탕으로 시간논리에 기반을 둔 자원 선점과 분배 규칙으로 구성된다.트 시스템은 b-Cart를 기반으로 할 것으로 예측할 수 있다.타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data b