• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.312-319
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• 2002

A recommendation system tracks past action of a group of users to make a recommendation to individual members of the group. The computer-mediated marking and commerce have grown rapidly nowadays so the concerns about various recommendation procedure are increasing. We introduce a recommendation methodology by which Korean department store suggests products and services to their customers. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is to select target customers, who have high purchase possibility of recommended products. Product taxonomy and association rule mining are used to select proper products. The validity of our recommendation methodology is discussed with the analysis of a real Korean department store.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.15-18
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• 2001

기존의 Association Rule 의 적용은 각 사건들이 고유한 연관관계를 갖는 다는 전재 하에 이를 이용하여 Data Mining Association Rule(연관규칙)을 적용해 왔다. 만약 이러한 연관규칙이 포함하지 않는 데이터에 대해서는 기존의 Rule 을 이용하기 위해서는 현재의 데이터를 재구성해야만 하는 필요성이 존재를 해왔다. 본 논문에서는 위와 같은 데이터의 재 구성없이 연관규칙을 포함하지 않은 데이터로부터 새로운 알고리즘을 이용하여 기존의 Association Rule 을 적용하고자 한다.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.19-25
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• 2005

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.365-377
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• 2006

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.33-43
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• 2013

The research on data mining that can manage a large amount of information efficiently has grown with the drastic increment of information. Privacy-preserving data mining can protect the privacy of data owners. There are several privacy-preserving association rule, clustering and classification protocols. A privacy-preserving association rule protocol is used to find association rules among data, which is often used for marketing. In this paper, we propose a privacy-preserving k-bits inner product protocol based on Shamir's secret sharing.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.397-405
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• 2010

Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.246-250
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• 2005

Recommand Systems are being used by an ever-increasing number of E-Commerce to help customers find products to purchase. Recommend Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different customers tastes. In this paper, we design and build a Recommend System using the historical customer movie purchase transactions and extracts the knowledge needed to make association recommendations to new customers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.15-21
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• 2008

This paper presents a new association mining algorithm that uses two phase sampling for shortening the execution time at the cost of precision of the mining result. Previous FAST(Finding Association by Sampling Technique) algorithm has the weakness in that it only considered the frequent 1-itemsets in trimming/growing, thus, it did not have ways of considering mulit-itemsets including 2-itemsets. The new algorithm reflects the multi-itemsets in sampling transactions. It improves the mining results by adjusting the counts of both missing itemsets and false itemsets. Experimentally, on a representative synthetic database, the algorithm produces a sampled subset of results with an increased accuracy in terms of the 2-itemsets while it maintains the same 1uality of the data set.