• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.104-109
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• 2006

We can offer suitable information to users analyzing the pattern of users. An association rule is one of data mining techniques which can discover the pattern. We use an association rule which considers the web page visiting time and we should the pattern analyse of users. The offered method puts the weights in Web page visiting time of the user and produces an association rule. Weight is web page visiting time unit divide to total of web page visiting time. We offer rather meaningful result the association rule by Apriori algorithm. This method that proposes in the paper offers rather meaningful result Apriori algorithm

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.155-159
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• 2003

In order to compansate the problem of fragmentating data and disdaining small group of data in decision trees, a descriptive rule set discovery method is suggested. The principle of association rule finding algorithm is presented and a modified association nile finding algorithm for efficiency is applied to target database which has condition and decision attributes to see the effect of modification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.25-28
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• 2006

사용자 의도 파악 (intention reading) 기술은 스마트 홈과 같은 복잡한 유비쿼터스(ubiquitous) 환경에서 사용자에게 보다 편리하고 개인화된(personalized) 서비스 제공이 가능하도록 해준다. 또한 학습 기능(learning capability)은 지식 발견(knowledge discovery)의 관점에서 의도 파악 기술의 핵심 요소 기술의 하나로 자리 매김 하고 있다. 본 논문에서는 스마트 홈 환경에서 제공 가능한 개인화된 서버스(personalized service) 중의 하나로, 개인화된 미디어 제어 방법에 대한 내용을 다룬다. 특히, 이러한 사람의 행동 패턴과 같은 데이터는 패턴 분류의 관점에서 구분해야 할 클래스(class)에 비해 입력 정보가 불충분할 경우가 많으므로 비일관적인(inconsistent) 데이터가 많으므로, 퍼지 논리(fuzzy logic)와 확률(probability)의 개념을 효과적으로 병행해야 의미 있는 지식을 추출해 낼 수 있다. 이를 위하여 반복 퍼지 지도 클러스터링 (IFCS; Iterative Fuzzy Clustering with Supervision) 알고리즘에 기반하여 주어진 데이터 패턴으로부터 확률적 퍼지 룰(probabilistic fuzzy rule)을 얻어 내는 방법에 대해 설명한다. 또한 이를 포함하는 학습 제어 시스템을 통해 개인화된 미디어 서비스를 추천해 줄 수 있는 방법에 대해서 설명하도록 한다.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.299-306
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• 2000

General definition of data mining is the knowledge discovery or is to extract hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important work is to find association rules in data mining. The objective of this paper is to find customer's trend using association rule from analysis of database and the result can be used as fundamental data for CRM(Customer Relationship Management). This paper uses Apriori algorithm and FoodMart data in order to find association rules.

• Genomics & Informatics
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• v.16 no.3
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• pp.44-51
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• 2018

Fluoroquinolone (FQ) antibiotics are an important class of synthetic antibacterial agents. These are the most extensively used drugs for treating bacterial infections in the field of both human and veterinary medicine. Herein, the antibacterial and pharmacological properties of four fluoroquinolones: lomefloxacin, norfloxacin, ciprofloxacin, and ofloxacin have been studied. The objective of this study was to analyze the antibacterial characteristics of the different fluoroquinolones. Also, the pharmacological properties of the compounds including the Lipinski rule of five, absorption, distribution, metabolism, and excretion, LD50, drug likeliness, and toxicity were evaluated. We found that among all four FQ molecules, ofloxacin showed the highest antibacterial activity through in silico assays with a strong interaction (-38.52 kJ/mol) with the antibacterial target protein (topoisomerase-II DNA gyrase enzyme). The pharmacological and pharmacokinetic analysis also showed that the compounds ciprofloxacin, ofloxacin, lomefloxacin and norfloxacin have good pharmacological properties. Notably, ofloxacin was found to possess an IGC50 (concentration needed to inhibit 50% growth) value of $0.286{\mu}g/L$ against the Tetrahymena pyriformis protozoa. It also tested negative for the Ames toxicity test, showing its non-carcinogenic character.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.46-52
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• 2005

In this paper, we present a temporal association rule based on item time intervals. A temporal association rule is an association rule that holds specific time intervals. If we consider itemset in the frequently purchased period, we can discover more significant itemset satisfying minimum support. Because the previous study did not consider the time interval between purchased item, it could find itemset that did not satisfy the minimum support in case some item was frequently purchased in a specific period and rarely or not purchased in other period. Our approach uses interval support which is counted by period with support and confidence in the association rule to discovery large itemset.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.903-911
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• 2008

Association rule mining searches for interesting relationships among items in a given large database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. Many data is most quantitative data. There is a need for partitioning techniques to quantitative data. The partitioning process is referred to as binning. We introduce several binning methods ; parameter mean binning, equi-width binning, equi-depth binning, clustering-based binning. So we apply these binning methods to several distribution types of quantitative data and present the best binning method for association rule discovery.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.336-342
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• 2001

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts decision processes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.128-132
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• 2001

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

• Journal of The Korean Association For Science Education
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• v.24 no.6
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• pp.1106-1117
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• 2004

The purposes of this study were to analyze types of scientific emotion word and to investigate the relationship between the ISE(Index of Scientific Emotion) and the ability of science-knowledge generation in subjects' scientific observation and rule-discovery. The subjects were asked to perform four scientific tasks. The tasks were developed that are suitable for scientific observation and rule-discovery. In performing tasks, the subjects were asked to describe their generated science-knowledge and scientific emotion through self-report questionnaire, performing each task. The strength of their scientific emotion was also measured using adjective emoticon check lists. In subjects' scientific observing, they showed 33.3% of interest emotion which was the biggest, 15.0% of acceptance emotion, and 11.3% of love emotion, respectively. In scientific rule-discovering, types of emotion were shown as 23.8% of interest, 21.5% of disgust, and 10.8% of acceptance, respectively. In addition, ability of science-knowledge generation was significantly correlated to ISE.