• Journal of Digital Convergence
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• v.17 no.9
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• pp.375-381
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• 2019

Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.191-198
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• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.717-726
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• 2003

In this paper, we analyze the associated rule based deductive algorithm which creates the rules automatically for intrusion detection from the vast packet data. Based on the result, we also suggest the deductive algorithm which creates the rules of intrusion pattern fast in order to apply the intrusion detection systems. The deductive algorithm proposed is designed suitable to the concept of clustering which classifies and deletes the large data. This algorithm has direct relation with the method of pattern generation and analyzing module of the intrusion detection system. This can also extend the appication range and increase the detection speed of exiting intrusion detection system as the rule database is constructed for the pattern management of the intrusion detection system. The proposed pattern generation technique of the deductive algorithm is used to the algorithm is used to the algorithm which can be changed by the supporting rate of the data created from the intrusion detection system. Fanally, we analyze the possibility of the speed improvement of the rule generation with the algorithm simulation.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.41-56
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• 2005

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance. we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.65-72
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• 2012

In the 21st century of information systems, the amount of information resources are ever increasing and the role of information searching system is becoming criticalto easily acquire required information from the web. Generally, it requires the user to have enough pre-knowledge and superior capabilities to identify keywords of information to effectively search the web. However, most of the users undertake searching of the information without holding enough pre-knowledge and spend a lot of time associating key words which are related to their required information. Furthermore, many search engines support the keywords searching system but this only provides collection of similar words, and do not provide the user with exact relational search information with the keywords. Therefore this research report proposes a method of offering expanded user relationship search keywords by analyzing user query patterns to provide the user a system, which conveniently support their searching of the information.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.99-109
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• 2010

Existing research about automated wafer transport management strategy for semiconductor manufacturing equipment was mainly focused on dispatching rules which is optimized to specific system layout, process environment or transfer patterns. But these methods can cause problem as like requiring additional rules or changing whole transport management strategy when applied to new type of process or system. In addition, a lack of consideration for interconnectedness of the added rules can cause unexpected deadlock. In this study, in order to improve these problems, propose dynamic priority based transfer job decision making algorithm which is applicable with regardless of system lay out and transfer patterns. Also, extra rule handling part proposed to support special transfer requirement which is available without damage to generality for maintaining a consistent scheduling policies and minimize loss of stability due to expansion and lead to improve productivity at the same time. Simulation environment of Twin-slot type semiconductor equipment was built In order to measure performance and examine validity about proposed wafer scheduling algorithm.

• Information Systems Review
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• v.8 no.2
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• pp.119-138
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• 2006

Association Rule Mining has been successfully used for mining knowledge in static environment but it provides limited features to discovery time-dependent knowledge from multi-point data set. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different multi-point snapshots. This paper proposes a procedure named 'Recursive Change Mining' for detecting continuous change of customer purchase behavior. The Recursive Change Mining Procedure is basically extended association rule mining and it assures to discover continuous and repetitive changes from data sets which collected at multi-periods. A case study on L department store is also provided.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.951-954
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• 2002

최근의 침입탐지시스템(IDS: Intrusion Detection System) 기술동향은 Misuse 방식의 규칙 데이터베이스 변경에 대한 한계성 때문에 Anomaly 방식의 NIDS(Network IDS)에 대한 연구가 고려되고 있다. 현재 국내에서 개발된 기존의 제품들은 대부분 Misuse 방식을 채택하고 있으며, 향후 국제 경쟁력을 갖추기 위해서는 Anomaly 방식의 기술 연구가 필요하다. 본 연구에서는 본 연구실에서 개발한 NIDS를 기반으로 연관 마이닝을 이용한 비정상 탐지 문제, 내부 정보 유출 차단 등에 대한 통합된 시스템 설계 방향을 제시하여 국가기관이나 기업이 보다 안전하게 침입을 관리할 수 있는 IPS(Intrusion Prevention System) 시스템을 설계한다.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.