• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.81-88
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• 2020

Various methods of differentiating and servicing digital content for individual users have been studied. Searching for association rules is a very useful way to discover individual preferences in digital content services. The Apriori algorithm is useful as an association rule extractor using frequent itemsets. However, the Apriori algorithm is not suitable for application to an actual content service because it considers only the reference count of each content. In this paper, we propose a new algorithm based on the Apriori that searches association rules by using purchase history and usage-time for each item. The proposed algorithm utilizes the usage time with the weight value according to purchase items. Thus, it is possible to extract the exact preference of the actual user. We implement the proposed algorithm and verify the performance through the actual data presented in the actual content service system.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.311-316
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• 2010

This paper presents a weight adjusted association rule mining algorithm (WARM). Assigning weights to each strategic factor and normalizing raw scores within each strategic factor are the key ideas of the presented algorithm. It is an extension of the earlier algorithm TSAA (transitive support association Apriori) and strategic importance is reflected by considering factors such as profit, marketing value, and customer satisfaction of each item. Performance analysis based on a real world database has been made and comparison of the mining outcomes obtained from three association rule mining algorithms (Apriori, TSAA, and WARM) is provided. The result indicates that each algorithm gives distinct and characteristic behavior in association rule mining.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.149-150
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• 2018

본 논문에서는 연관 규칙 마이닝 알고리즘인 Apriori을 이용하여 컨트롤러를 선택하는 모델을 제안하였다. 제안 모델은 모든 컨트롤러 정보를 수집한 다음 발생 지지도(Transaction support)를 이용하여 컨트롤러의 실행 빈도를 측정한다. 이를 통해 연관된 컨트롤러를 동시에 실행함으로써 효율적인 컨트롤러 선택을 기대한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.39-40
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• 2019

본 논문에서는 연관규칙 마이닝 알고리즘인 Apriori 알고리즘을 기반으로 향상된 인공벌 군집 알고리즘(ABC algorihtm)을 적용하여 SDN 환경에서 분산된 컨트롤러를 선택하는 모델을 제안하였다. 이를 통해 자주 사용되는 컨트롤러를 우선적으로 선택함으로써 향상된 컨트롤러 선택을 목표로 한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1129-1143
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• 2009

This paper proposes a recommendation system (Association Rule System for Targeting) which considers target which is not considered by previous Logistic Regression system, and proves that the efficiency of the recommendation system is better than that of the current and previous Apriori algorithm system. Also this study shows that the click and purchasing rate of the proposed Association Rule System for Targeting is much higher than those of current Apriori algorithm system after the purchasing campaign even though the open rate of the former is lower than that of the latter. In comparison with Logistic Regression methodology, this paper proves with experimental data that the purchasing effect of the proposed system for specific items is much higher in accuracy than that of current Apriori algorithm system even though the purchasing rate of current Apriori algorithm system is higher in whole shopping malls than that of the proposed Association Rule System for Targeting.

• International journal of advanced smart convergence
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• v.1 no.1
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• pp.43-47
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• 2012

Parkinson's disease is representative degenerative diseases of the nervous system, which is from deficiency of dopamine neurons to pass in which the gradual degeneration of the body. In this paper, open UCI repository data of Parkinson's patients is used for experiments. The classification based on correlation analysis is examined. In addition, the relationship between groups is differentiated by cluster analysis based on patients with Parkinson's disease by apriori algorithm and correlation analysis. It is used to find the properties that distinguish cluster analysis. Though the disease is the same in the basic structure, each group is compared as each gender group with the most distinctive part of the characteristics.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.61-71
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• 2007

The Association Rules is the most important method in technology of the data mining. This text further study The Association Rules, has analyzed and commented to Apriori algorithm of The Association Rules. Have realized Apriori algorithm base on Visual Basic 6.0, probe into Apriori algorithm application among the digital library, show with experimental data of application of Association Rules in borrow in the data analysis in readers finally.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.77-87
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• 2023

Applying various association rule mining algorithms to the network intrusion detection task involves two critical issues: too large size of generated rule set which is hard to be utilized for IoT systems and hardness of control of false negative/positive rates. In this research, we propose an association rule mining algorithm based on the newly defined measures called coverage and exclusion. Coverage shows how frequently a pattern is discovered among the transactions of a class and exclusion does how frequently a pattern is not discovered in the transactions of the other classes. We compare our algorithm experimentally with the Apriori algorithm which is the most famous algorithm using the public dataset called KDDcup99. Compared to Apriori, the proposed algorithm reduces the resulting rule set size by up to 93.2 percent while keeping accuracy completely. The proposed algorithm also controls perfectly the false negative/positive rates of the generated rules by parameters. Therefore, network analysts can effectively apply the proposed association rule mining to the network intrusion detection task by solving two issues.