• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Journal of Korea Game Society
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• v.19 no.1
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• pp.95-108
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• 2019

The purpose of this study is to suggest ways to design gamification to improve the game and digital addiction. For the study, 782 of gmaification cases were collected and game mechanics and fun experience were analyzed by 4F process which is to design the effective gamification. To find the specific pattern, apriori algorithm, which is to find associated rules in transaction is applied to the 782 cases. According to the results, 63 of game mechanics associated rules are found. In the fun experience, 37 of associated rules are found. Based on the result, this study suggest the direction of gamification design for game and digital addiction improvement.

• Smart Media Journal
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• v.7 no.4
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• pp.99-105
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• 2018

It is often the case that products or services on the market are causing problems, which hurt the business and image of the company. Responding appropriately to the problem and minimizing the damage is very important to business organizations. This study collected and analyzed the news articles related to the recall of the Galaxy Note 7, which was developed and launched by Samsung Electronics, one of the smartphone market leaders. Based on the issue lifecycle, the characteristics of the news were expressed by stages and the contents of the news were analyzed and visualized using association rules. The results of this study are expected to help business organizations to understand the changes and trends of issues and search for counter measures.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• The Journal of Korean Association of Computer Education
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• v.16 no.2
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• pp.61-68
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• 2013

SNS is a social networking service that helps people to have a two-way communication, manage their personal relationships and share information. The domestic and international SNS markets have attained a steady growth, and their growth is being more accelerated recently. Under these circumstances, immature students are more likely to show negative cyber behavior. This study attempted to analyze the relationship between the use of SNS, motives of SNS use, the use of active SNS functions, SNS-dependency and views in SNS and negative SNS behaviors among elementary and middle school students. For this, negative cycber behaviors are classified into four stages depending on the severity, for each of which distribution of factors is investigated and the combination of factors to determine each stage is obtained through association rule analysis. As a result, it is found that 85% of the students rarely show negative cyber behaviors, stealing personal information and contacting with strangers are the most frequent negative behaviors, and students with a great dependency on SNS are highly probable to show negative behaviors.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.282-289
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• 2008

Although there were some technological developments in improving the collaborative filtering, they have yet to fully reflect the actual relation of the items. In this paper, we propose the recommendation system using associative web document classification by word frequency and ${\alpha}$-cut to address the short comings of the collaborative filtering. The proposed method extracts words from web documents through the morpheme analysis and accumulates the weight of term frequency. It makes associative rules and applies the weight of term frequency to its confidence by using Apriori algorithm. And it calculates the similarity among the words using the hypergraph partition. Lastly, it classifies related web document by using ${\alpha}$-cut and calculates similarity by using adjusted cosine similarity. The results show that the proposed method significantly outperforms the existing methods.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.423-432
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• 2011

In this paper, we introduce XOnto-Apriori algorithm which is an extension of the Onto-Apriori algorithm. The extended algorithm is designed to improve the conventional algorithm's problem of comparing only identifiers of transaction items by reasoning transaction properties of the items which belong in the same category. We show how the mining algorithm works with a smartphone application recommender system based on our extended algorithm to clearly describe the procedures providing personalized recommendations. Further, our simulation results validate our analysis on the algorithm overhead, precision, and recall.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.362-365
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• 2006

인터넷 사용자가 급증함에 따라 온톨로지를 이용한 지능형 웹이나 인터넷 사용자에게 개인 맞춤형 서비스를 제공하기 위한 다양한 연구가 진행되고 있다. 대표적인 예로 문맥광고는 인터넷 사용자들이 뉴스나 커뮤니티 사이트에서 콘텐츠를 조회하고, 해당 콘텐츠와 일치하거나 관련성이 높은 제품 또는 서비스 정보를 제공하는 광고기법이다. 그러나 문맥 광고는 사용자에게 다양한 콘텐츠 및 사이트 추천 서비스를 제공하지 못하고 있다. 따라서 다양한 콘텐츠 및 사이트 추천 서비스를 제공하기 위해 본 논문에서는 사용자가 조회한 콘텐츠의 내용을 대표할 수 있는 중요 키워드를 선정하고, 콘텐츠 내에서 추출된 키워드간의 연관성을 분석하여 관련 콘텐츠 및 사이트를 추천하는 방법에 대해 제안한다. 또한 연관키워드리스트 생성방법을 고속연관규칙을 이용하여 처리속도를 줄이고, 사용자가 선호할 만한 다양한 콘텐츠와 관련된 사이트를 제공하는 방법에 대해 제안한다.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.1-22
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• 2022

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.