• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.158-163
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• 2012

This paper proposes an FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) design that detects anomaly and misuse attacks by using a Naive Bayesian algorithm, Data Mining, and Fuzzy Logic. The NB-AAD(Naive Bayesian based Anomaly Attack Detection) technique using a Naive Bayesian algorithm within the FHIDS detects anomaly attacks. The DM-MAD(Data Mining based Misuse Attack Detection) technique using Data Mining within it analyzes the correlation rules among packets and detects new attacks or transformed attacks by generating the new rule-based patterns or by extracting the transformed rule-based patterns. The FLD(Fuzzy Logic based Decision) technique within it judges the attacks by using the result of the NB-AAD and DM-MAD. Therefore, the FHIDS is the hybrid attack detection system that improves a transformed attack detection ratio, and reduces False Positive ratio by making it possible to detect anomaly and misuse attacks.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.299-308
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• 2006

As recently XML is becoming the standard of exchanging web documents and public documentations, XML data are increasing in many areas. To retrieve the information about XML documents efficiently, the semantic web based on the ontology is appearing. The existing ontology has been constructed manually and it was time and cost consuming. Therefore in this paper, we propose the semi-automatic ontology generation technique using the data mining technique, the association rules. The proposed method solves what type and how many conceptual relationships and determines the ontology domain level for the automatic ontology generation, using the data mining algorithm. Appying the association rules to the XML documents, we intend to find out the conceptual relationships to construct the ontology, finding the frequent patterns of XML tags in the XML documents. Using the conceptual ontology domain level extracted from the data mining, we implemented the semantic web based on the ontology by XML Topic Maps (XTM) and the topic map engine, TM4J.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.328-337
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• 2022

The importance of online marketing is emerging due to the prevalence of COVID-19. In order to respond to the changing business environment, we have collected ten years of sales data of SME bakery company that have experienced a decrease in sales due to the COVID-19. As a result of the analysis, we found that switching from offline markets to omnichannel B2B and B2C markets and taking 'small quantity batch production' to 'mass production in a small variety can improve management. This study presented online and offline marketing strategies through data analysis of small and medium-sized bakery companies, which have relatively insufficient digital capabilities compared to large companies, and could be a guideline for many SMEs.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.471-474
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• 2007

본 논문은 연관규칙탐사 기술에서 사용되는 빈발항목집합과 동일한 개념으로 문서분류의 문서에서 빈발단어집합을 정의하고, 이를 사용하여 문서분류 방법으로 잘 알려진 kNN에 적용하였다. 이를 위하여 하나의 문서는 여러 개의 문단으로 나뉘어졌으며, 각 문단에 나타나는 단어들의 집합을 트랜잭션화하여 빈발단어집합을 찾을 수 있도록 하였다. 제안한 방법은 AI::Categorizer 프레임워크에서 구현되었으며 로이터-21578 데이터를 사용하여 학습문서의 크기에 따라 그 정확도가 측정되었다. 정확도의 측정된 결과로 부터 제안된 방법이 기존의 방법에 비하여 정확도를 개선한다는 사실을 알 수 있었다.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.13-32
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• 2018

The purpose of this study is to analyze the association among the subject areas of big data research papers. The subject group of the units of analysis was extracted by applying co-citation networks, and the rules of association were analyzed using Apriori algorithm of R program, and visualized using the arulesViz package of R program. As a result of the study, 22 subject areas were extracted and these subjects were divided into three clusters. As a result of analyzing the association type of the subject, it was classified into 'professional type', 'general type', 'expanded type' depending on the complexity of association. The professional type included library and information science and journalism. The general type included politics & diplomacy, trade, and tourism. The expanded types included other humanities, general social sciences, and general tourism. This association networks show a tendency to cite other subject areas that are relevant when citing a subject field, and the library should consider services that use the association for academic information services.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.155-167
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• 2006

It is desirable if users surfing on the Internet could find Web pages related to their interests as closely as possible. Toward this ends, this paper presents a topic specific Web crawler computing the degree of relevance. collecting a cluster of pages given a specific topic, and refining the preliminary set of related web pages using term frequency/document frequency, entropy, and compiled rules. In the experiments, we tested our topic specific crawler in terms of the accuracy of its classification, crawling efficiency, and crawling consistency. First, the classification accuracy using the set of rules compiled by CN2 was the best, among those of C4.5 and back propagation learning algorithms. Second, we measured the classification efficiency to determine the best threshold value affecting the degree of relevance. In the third experiment, the consistency of our topic specific crawler was measured in terms of the number of the resulting URLs overlapped with different starting URLs. The experimental results imply that our topic specific crawler was fairly consistent, regardless of the starting URLs randomly chosen.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.33-39
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• 2022

This study aims to investigate the online class preference depending on students' gender and school level. To achieve this aim, the study conducted a survey on 4,803 elementary, middle, and high school students in 17 regions nationwide. The valid data of 4,524 were then analyzed using the Apriori algorithm to discern the associated patterns of the online class preference corresponding to their gender and school level. As a result, a total of 16 rules, including 7 from elementary school students, 4 from middle school students, and 5 from high school students were derived. To be specific, elementary school male students preferred software-based classes whereas elementary female students preferred maker-based classes. In the case of middle school, both male and female students preferred virtual experience-based classes. On the other hand, high school students had a higher preference for subject-specific lecture-based classes. The study findings can serve as empirical evidence for explaining the needs of online classes perceived by K-12 students. In addition, this study can be used as basic research to present and suggest areas of improvement for diversifying online classes. Future studies can further conduct in-depth analysis on the development of various online class activities and models, the design of online class platforms, and the female students' career motivation in the field of science and technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.365-368
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• 2006

심장의 활동을 기록한 심전도는 심장의 상태에 대한 가치 있는 임상 정보를 제공한다. 지금까지 심전도를 이용한 심장 질환 진단 알고리즘에 대한 많은 연구가 진행되어 왔으나, 심장 질환에 대한 진단 결과의 부 정확성으로 인해 외국의 진단 알고리즘을 사용하고 있다. 이 논문에서는 원시 심전도 데이터로부터 심장 질환 진단의 파라미터인 ST-segment 추출 방법을 제안한다. ST-segment는 관상동맥질환 예측에 활용되므로 데이터마이닝의 분류기법을 적용하여 질환을 예측한다. 또한 연관규칙 마이닝을 통해 환자들의 임상 데이터로부터 심장 질환자들의 임상적 특징을 예측한다.