• Management & Information Systems Review
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• v.31 no.3
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• pp.21-46
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• 2012

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.61-69
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• 2015

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.1
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• pp.29-41
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• 2024

Objectives: This study used an efficient data mining algorithm to explore association rules between the lifestyle risk behaviors and multimorbidity (having more than one chronic disease) in Korean adults. Methods: We used data from the 8th Korean National Health and Nutrition Examination Survey(2019-2020) for 7,609 adults aged ≥19 years. This study was undertaken where 6 lifestyle risk behaviors and 11 morbidities were analyzed using R and Rstudio for the ARM. Results: Among 117 association rules, combinations of hypertension, dyslipidemia and diabetes, hypertension were important role in inadequate sleep, physical inactivity and inadequate weight. Conclusion: The findings of this study are significant because they demonstrate the importance of lifestyle risk factors and the role of multiple chronic diseases using big data analytics such as association rule mining. We recommend developing selective and focused health education programs, such as exercise programs to address physical inactivity, dietary interventions to address inadequate weight, and mental health education programs to address inadequate sleep.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.49-51
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• 2001

데이터 마이닝은 KDD의 분야로서, 의미 있는 정보와 관심 있는 행동 패턴을 추출해 나가는 과정이다. WWW의 발전으로, 웹 데이터가 거대해지고 있다. 이러한 데이터 마이닝 분야에서도, 웹 사용 마이닝의 목적은 의미 있는 사용자 행동 패턴을 찾아내는 것이다. 특히 현재 전자상거래가 널리 활성화되고 있는 환경에서, 사용자의 특성을 발견해내는 것은 매우 중요한 부분이다. 사용자의 특성에 따라 사용자에게 상품을 추천하거나 메일을 보내는 것이나 사용자에게 적절하게 사이트를 구축하는 것이 가능하다. 전처리 과정을 통해서 추출된 트랜잭션 데이터를 모호한 사용자의 요구를 분석할 수 있는 퍼지 집합으로 변형시켜 Fuzzy Association Rule을 통해 분석한다. 그리고 분석된 결과에 대한 규칙을 사용자의 피드백을 통해서 다시 분석하는 과정을 거치게 된다. 사용자의 요구 사항을 적절히 반영할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.133-135
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• 2003

본 연구에서는 자바 코드로부터 UML 클래스 다이어그램을 추출하는 역공학방법을 제시하였다. 파서를 이용하여 자바 코드로부터 AST를 생성하고 이를 순회하면서 클래스다이어그램 생성에 필요한 정보를 추출하였다. 이를 위해 구조정보와 관계정보를 정의하였는데, 구조정보에서는 클래스 몸체를 구성하는 정보를 표현하였다. 관계정보에서는 클래스들 간의 연관관계를 결정하기 위해 필요한 정보를 표현하였으며, 얻어진 관계정보를 통해 연관관계를 유추하는 방법을 제시하였다. 특히 클래스들간의 연관관계를 추출하기 위한 규칙들을 정의하고, 이를 통해 얻어진 관계정보를 이용하여 연관관계를 유출하는 과정을 설명하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.502-505
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• 2000

본 논문에서는 상태 전이 분석과 연관 마이닝 기법을 이용하여 새로운 침입 탐지 알고리즘인 침입 시나리오 자동 생성 알고리즘(Automatic Generation Algorithm of the Penetration Scenarios : AGAPS)을 개발하고자 한다. 침입을 탐지하기 위하여, 먼저 상태 전이 기법을 이용하여 네트워크를 통해 전달된 명령어들에 대한 상태 테이블을 생성한다. 그리고 연관 마이닝 기법을 이용하여 명령어들의 연관 규칙을발견한 후, 이러한 명령어들이 불법 침입과 관련된 명령어들인지를 판별함으로서 불법 침입 여부를 판단한다.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.223-234
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• 2020

This research is the association rule analysis using Apriori algorithm of data mining focusing on 32 predictive key words extracted from Hong (2019) affecting successful aging in Korea. And, to examine rules and patterns of those key words or predictive variables, this research used support, confidence, and lift. The data was analyzed with the R version 3. 5. 1 program, and visualized using arulesViz package and visNetwork. It was found that the variables highly associated with successful aging in Korea were 'hobby', 'volunteer service', 'preparation', and 'exercise'. This research concludes that, the variable which needs to be considered first of all for successful aging in Korea is 'hobby', followed by 'volunteer service', 'preparation', and 'exercise'.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.261-271
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• 2001

정보 공유와 비즈니스 수행 등의 매체로서 World Wide Web의 이용이 보편화됨에 따라 다양하고 방대한 데이터를 웹을 통하여 얻을 수 있게 되었으며, 이러한 데이터로부터 유용한 정보를 추출하기 위한 데이터 분석과 활용은 많은 분야에서 중요한 사안으로 인식되고 있다. 본 연구에서는 웹 로그(web log)데이터로부터 정보를 추출하기 위한 과정 및 방안에 대해 살펴보고자 한다. 로그 데이터의 특징과 통계 데이터와의 차이점, 데이터 수집 및 사전 처리 과정, 추출할 수 있는 정보 및 분석 방법 등을 제시하고 로그 데이터 분석 예제를 제시한다.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.455-462
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• 2004

Network attacks are various types with development of internet and are a new types. The existing intrusion detection systems need a lot of efforts and costs in order to detect and respond to unknown or modified attacks because of detection based on signatures of known attacks. In this paper, we present a design and implementation for mining prototype system to predict unknown or modified attacks through network protocol attributes analysis. In order to analyze attributes of network protocols, we use the association rule and the frequent episode. The collected network protocols are storing schema of TCP, UDP, ICMP and integrated type. We are generating rules that can predict the types of network attacks. Our mining prototype in the intrusion detection system aspect is useful for response against new attacks as extra tool.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.