• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1089-1098
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• 2007

Currently, user require is diverse on the Web. Furthermore, each web user is wishing to retrieve data or goods that hey want to look for more conveniently and more quickly. Because different search criteria and dispositions of web users, they lead to unnecessary repeated operations in order to use implemented by web designer. In this paper, we suggest the system that analyzes user patterns on the Web using the technique of log file analysis and transfers more effectively the information of web sites to users. And we analyze the log file for customer data in the system the proposed method are implemented by means of EC-Miner that is one of the tool of datamining, and aims to offer appropriate Layout corresponding with personalization by giving weight to each transport path.

• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.339-349
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• 2012

Smartphones which represent to the Mobile convergence, is the rapid spread, more than half of Korean are using. Accordingly, mobile apps that run on smartphone market is growing at a rapid pace. However, most studies on smartphone and mobile apps are focusing on the technology acceptance and improvement of function. So, this study is to suggest promotion strategy to each mobile apps, analyzed through three phases. First phase is the frequency analysis that deduct most frequently used mobile apps. Second phase is association rules that found to associate between mobile apps. Finally, to analyze deduction techniques for acquired 5 mobile apps to target variable in pre-2 phase use total 35 variables of 20 mobile apps categories, demographic variables, amount of PC, movie, music, book, game usage and fees per month.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.969-982
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• 2016

Recently, the researches to discovery drug candidates from natural herbs have received considerable attention. In human body, enzyme mostly metabolize the compounds of natural herbs. In this study, we analysis the enzyme interactions using assoication mining. We get this data from BRENDA(: BRaunschweig ENzyme DAtabase) system. Based on enzyme interaction model, we divide the metabolites into substrate metabolites, product metabolites, inhibitor metabolites, and activating metabolites. We then compose substrate metabolite transaction, product metabolite transaction with each metabolites and enzyme interaction transaction with all metabolites. Also we take account of organism for each transactions. We mine frequent metabolites and patterns from six transactions using association rule mining. And we analysis the relationship among metabolites. As a result, we identify the distributions and patterns of metabolites consist in enzyme interactions. We found that metabolites include in only substrate are identified and have very low supports. This results can be useful to develop the effective metabolism prediction model for compounds of natural herbs.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.328-332
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• 2000

As DNA sequences have been known through the Genome project the techniques for dealing with molecule-level gene information are being made researches briskly. It is also urgent to develop new computer algorithms for making databases and analyzing it efficiently considering the vastness of the information for known sequences. In this respect, this paper studies the association rule search algorithms for finding out the characteristics shown by means of the association between promoter sequences and genes, which is one of the important research areas in molecular biology. This paper treat biological data, while previous search algorithms used transaction data. So, we design a transformed association nile algorithm that covers data types and biological properties. These research results will contribute to reducing the time and the cost for biological experiments by minimizing their candidates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.739-744
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• 2000

As DNA sequences have been known through the Genome project the techniques for dealing with molecule-level gene information are being made researches briskly. It is also urgent to develop new computer algorithms for making databases and analyzing it efficiently considering the vastness of the information for known sequences. In this respect, this paper studies the association rule search algorithms for finding out the characteristics shown by means of the association between promoter sequences and genes, which is one of the important research areas in molecular biology. This paper treat biological data, while previous search algorithms used transaction data. So, we design a transformed association rule algorithm that covers data types and biological properties. These research results will contribute to reducing the time and the cost for biological experiments by minimizing their candidates.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.87-98
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• 2006

The reform of the information technique is very decisive in reforming effort of the government, and ubiquitous government is a representative example. But a problem in ubiquitous government service is that the efficiency is low. The reason is that the service of ubiquitous government could not be provided with a corresponding service to the inhabitants which is real and actual user. from now on, Gyeongnam province must have the ubiquitous service plan which can be the corresponding to the need of the inhabitants. In this paper we survey a ubiquitous recognition of Gyenongnam inhabitants and analyze the present situation by association rule mining. We can offer a basic data of policy about a ubiquitous service construction of Gyeongnam from the results of this paper.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.945-952
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• 2001

In consequence of expansion of internet users, electronic commerce is becoming a new prototype for marketing and sales, arid most of electronic commerce sites or internet shopping malls provide a rich source of information and convenient user interfaces about the organizations customers to maintain their patrons. One of the convenient interfaces for users is service to recommend products. To do this, they must exploit methods to extract and analysis specific patterns from purchasing information, behavior and market basket about customers. The methods are association rules and sequential patterns, which are widely used to extract correlation among products, and in most of on-line electronic commerce sites are executed with users information and purchased history by category-oriented. But these can't represent the diverse correlation among products and also hardly reflect users' buying patterns precisely, since the results are simple set of relations for single purchased pattern. In this paper, we propose an efficient mining technique, which allows for multiple purchased patterns that are category-independent and have relationship among items in the linked structure of single pattern items.