Nowadays, due to escalating the demands of the direct purchase of e-commerce with many kinds of item, the demand for e-commerce is exploding. The recommender system finds items for customer easily and targets in customers for the e-commerce firms easily by an automated recommending process. And also, association rules are often used to facilitate product sales in marketing pattern analysis through recommender systems in e-commerce. This paper takes aim at a new recommending service in e-commerce using periodicity analysis of weighted sequential pattern. In e-commerce, we need the FRAT segmentation method based on the various items purchased by the customer, to reflect the weights and perform the pre-processing tasks using sequential patterns for period analysis. We apply an effective incremental sequential data mining method that adds incremental purchasing data as a change of the four season changes. As a result, we suggest a recommendation service for micro-marketing of e-commerce based on big data analysis for promoting e-commerce purchasing, which satisfies customer's taste with various products of e-commerce. To check the performance of the proposal, we tested the data set with the same conditions as before. As a result, the proposed system is more efficient than the other systems in the results of system evaluation.

Background/Objectives: Nowadays, many people enjoy the direct purchase on e-commerce which many kinds of item are increasing explosively. Existing method has a weakness of the accuracy to forecast, leaving customers unsatisfied. Methods/Statistical Analysis: We search association rule into customers’ buying behavior and discover items of group purchase which promoted cooperative buying. We can also create the table of association rules in the whole purchase history data to join customer’s record. If a customer would want additional sale for cross selling and up selling, the system could recommend the items which worked on the rate threshold of association rules to promote sale. Findings: We search association rule into customers\' purchase data and discover frequent pattern to promote sale for selling associated items, to forecast frequent pattern of customer’s interest of item. We need to make clustering the category of associated items to reflect the customer’s propensity to reflect customer’s interest of associated item as well as to create the table of association rules in the whole purchased history data to join customer’s record. Our proposing system is higher 19.15% in F-measure than existing system. We execute the preprocessing for clustering the category of associated items to reflect the customer’s propensity. Our proposing system is higher 27.42% in recall, even if it is lower 12.23% in precision than previous system which conducted the analysis of segmentation to have different weights, is advanced than existing system. Our proposing system is better in F-measure than both the previous system and existing system. We could recommend associated items which worked on the rate threshold of association rules to promote sale, if a customer bought associated items. Improvements/Applications: We have the improvement that proposing system is higher 12.54% in F-measure than existing system. We make application to promote sale for selling associated items in e-commerce.Keywords: Association Rules, Clustering, Collaborative Filtering, Segmentation Method

We developed recommending solution based on customer inclination using AHP (Analytic Hierarchy Process) to propose recommending service. Collaborative filtering adopts evaluation methods based on user profiles. However, these methods have the difficulties in analyzing the clients' inclinations, interests and levels, as well as cost problem, i.e. it leaves customers unsatisfied. We execute the preprocessing of clustering of user category with weight based on customer inclination. We conduct the system performance evaluation on the data set collected in a cosmetic internet shop. We had the improvement for proposing system.

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener’s prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener’s preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

Recently, the clinical and healthcare recommending service is required in medical center for the clinical diagnosis and plan of treatment in connection with cardiovascula disease. We propose a method of the clinical and healthcare recommending service based on cardiovascula disease pattern analysis for medical treatment service. We use SVM(Support Vector Machine) to segment the clinical historical data, to join patients’ clinical test data with input vectors of multi-parametric features, cardiovascula disease code, input factors and finally forms clusters of the clinical historical data based on electronic medical record. Then, we make an application on the clinical and healthcare recommending service for cardiovascula disease treatment information of cardiovascula patients to reduce patients’ search effort to get the curing information and the diagnosis for recovering their health, to improve the accuracy for the clinical and healthcare recommending service. We carry out experiments with data set of medical center to measure its performance. We report some of the experimental results.

This paper proposes an efficient recommending method for computerized treatment plan of cancer patients in Korea using electronic discharge summary of EMR in HL7. In this paper, it is necessary for us to classify disease patterns in the medical historical record to join the electronic discharge summaries data in EMR to analyze the disease pattern with input vectors of different features, disease code, to build the medical treatment plan of cancer patients in Korea using recommending service in medical data sets, to reduce inpatients' search effort to get the information of curing procedure, the diagnosis for recovering their health and to improve the rate of accuracy of recommending service. To verify improved performance, we make experiments with dataset collected in medical center.

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the listeners. In this paper, we propose music recommender system using learning listener's prefererece, such as Melon, Billboard, Bugs Music, Soribada, and Gini, with most popular current songs across all genres and styles. It is also necessary for us to make the task of calculating the preference with weight to reflect the preference of most popular current songs with its popular music charts on trends. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

This paper proposes a new recommending method using effective purchase pattern mining with weight based on FRAT (Frequency, Regency, Amount and Type of merchandise or service) analysis in e-commerce. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the task of mining frequent pattern in purchase data extracted the most frequently from whole data, to join customer’s data, to keep the analysis of FRAT to calculate the weigh and to make the task of clustering of item category in order to recommend item with an immediate effect by frequently changing trends of purchase pattern. We consider the importance of type of merchandise or service and then, suggest recommending method using mining frequent pattern with weight based on FRAT analysis to forecast frequently changing trends by emphasizing the important items with efficiency and to reflect different merchandises on e-commerce being extremely diverse for customers’ need. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

This paper proposes recommending method with an immediate effect through adaptive clustering method based on segmented merchandise code in e-commerce. Using an implicit method without onerous question and answer to the users, it is necessary for us to do the task of clustering using merchandise code in purchase data extracted from whole data, to join customer’s data, to keep the analysis of RFM (Recency, Frequency, and Monetary) in order to segment merchandise with an immediate effect by Bayesian suggestion to consider frequently changing customer’s preference. We reflect the importance of attribute for merchandise code and then take adaptive clustering of merchandise code customer prefer to forecast frequently changing customer’s preference of merchandise code efficiently. We carry out experiments with data set of internet cosmetic shopping mall to measure its performance. We report some of the experimental results.

This paper proposes an SOM clustering method using user's features to classify profitable customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify profitable customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the customers using SOM with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce customers' search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

유비쿼터스 컴퓨팅 환경하에서 전자상거래 대규모가 대형화되고 취급되는 항목제품들도 다종 다양해지고 있는 것이 현실이다. 이러한 유비쿼터스 상거래 시스템은 편리하고 신속하게 제공되어야 하고 다이나믹한 환경에서 실시간성과 민첩성이 요구되고 있다. 데이터마이닝에서 추출한 지식을 적극적으로 활용하는 기법들이 전자상거래에서 구매 촉진을 증진시키는 마케팅 전략으로 활용되고 있다. 본 연구에서는 유비쿼터스 컴퓨팅 환경 하에 지능형 모바일 단말기를 이용한 추천을 위한 가중치기반 순차패턴 탐사를 이용한 추천서비스f를 제안하였다. 본 연구에서는 추천의 정확성을 향상시키고 구매력이 높은 항목제품 및 서비스를 추천하기 위해서 FRAT 세분화 기법을 이용한 가중치기반 순차패턴 탐사를 이용한 추천서비스를 제안하였다. 성능평가를 위해 현업에서 사용하는 인터넷 화장품 쇼핑몰의 데이터를 기반으로 데이터 셋을 구성하여 기존의 방법과 비교 실험을 통해 성능을 평가하여 효용성과 타당성을 입증하였다. 유비쿼터스 상거래에서 시간과 장소에 제약을 받지 않는 모바일 웹앱을 이용한 추천서비스를 위해서 이전방법보다 개선된 방법으로 추천서비스를 구현하였다. Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

This paper proposes a new weighted mining frequent pattern based on customer’s RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing. An existing recommendation system using traditional mining has the problem, such as delay of processing speed from a cause of frequent scanning a large data, considering equal weight value of every item, and accuracy as well. In this paper, to solve these problems, it is necessary for us to extract the most frequently purchased data from whole data, to consider the weight/importance of attribute of item in order to forecast frequently changing trends by emphasizing the important items with high purchasability and to improve the accuracy of personalized u-commerce recommendation. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall. Keywords; RFM; Association Rules; Weighted Mining Frequent Itemsets using FP-tree;

This paper proposes an efficient purchase pattern clustering method based on SOM(Self-Organizing Map) for Personal Ontology Recommender System in u-Commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to keep clustering the user’s information to join the user’s score based on RFM factors using SOM network and the analysis of RFM to be able to reflect the attributes of the user in order to reflect frequently changing trends of purchase pattern by emphasizing the important users and items, and to improve better performance of recommendation. The proposed makes the task of an efficient purchase pattern clustering based on SOM for preprocessing so as to be possible to recommend by the loyalty of RFM factors as considering user’s propensity. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

A existing recommedation system using association rules has the problem, which is suffered from inefficiency by reprocessing of the data which have already been processed in the incremental data environment in which new data are added persistently. We propose the recommendation technique using incremental frequent pattern mining based on RFM in e-commerce. The proposed can extract frequent items and create association rules using frequent patterns mining rapidly when new data are added persistently.

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of ∙제1저자 : 조영성 ∙교신저자 : 문송철 ∙투고일 : 2013. 10.24, 심사일 : 2013. 11. 20, 게재확정일 : 2013.12. 3. * 동양미래대학교 전산정보학부(Dept. of Computer Science, Dongyang mirae University) ** 남서울대학교 컴퓨터학과(Dept. of Computer Science, Namseoul University) *** 충북대학교 전자컴퓨터공학부(School of Electrical and Computer Science, Chungbuk National University), 194 Journal of The Korea Society of Computer and Information February 2014 user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user’s features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user’s features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall. ▸

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

모든 의료정보시스템에는 이해관계자와 환경이 존재한다. 의료정보시스템 개발 시에는 이 같은 환경에서 사용자의 기능적 요구사항과 비기능적 요구사항인 품질을 만족시켜야할 목표가 있다. 이 목표를 달성하기 위하여 현재 다양한 방법으로 정보시스템 개발이 이루어지고 있고 다양한 애플리케이션이 등장하고 있다. 그러나 이 같은 의료정보시스템 개발의 과정에서 기본적인 요구조건을 만족하고 있는지는 별도의 관점에서 고찰하지 않으면 안 된다. 본 연구는 유-헬스케어 서비스 소프트웨어아키텍쳐 품질확보를 위한 요구사항 분석방법을 제안한다. 의료정보시스템의 요구사항 분석을 통해 소프트웨어아키텍처 품질평가 사항과 의료정보서비스 품질평가 연계지표 평가방식을 제안했다. 이 방법은 연계성 팩터의 품질 합계치를 산출하고 그 추이를 분석하므로서 유-헬스케어 소프트웨어아키텍쳐에 대한 종합평가가 가능하게 한다. 품질평가는 요구사항 분석에서 도출된 목표와 비교하여 달성도를 분석하며 만족도 수준이 미진한 분야를 발췌하여 원인분석 및 개선작업에 활용이 가능하다. All medical information system stakeholders and the environment exists. Medical information systems for development in these environments and non-functional requirements, functional requirements and quality goals are to be met. In order to achieve these goals in a variety of ways currently being made to develop information systems and various applications are emerging. However, the process of developing these health information systems meet the basic requirements and does not consider that from the point of view should not be separate. This study of the development of health information systems related to quality measurement indicators for the analysis software architectures, and medical information, information quality evaluation of service quality information associated indicators evaluation are offered. This way of associated indicators for the quality of the output sum and analyze the trends in software architecture u-Healthcare should be available for assessment. Quality score compared with pre-set goals for achievement and satisfaction levels of analysis further support the cause excerpt field use in analysis and improvement is possible.

This paper proposes a new personalized u-commerce recommending service using weighted sequential pattern with time-series and FRAT(Frequency, Regency, Amount and Type of merchandise or service) method under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the FRAT score and the task of mining sequential pattern with time-series in order to do recommending service based on periodicity analysis by timely changing trends of seasonable pattern, and to improve the accuracy of recommendation with high purchasability To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

This paper proposes a new clustering method using the weighted preference based on RFM(Recency, Frequency, Monetary) Score for personalized recommendation in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, not used user’s profile for rating, it is necessary for us to extract the most frequent purchase items from the whole purchase data and to calculate the weighted preference of item for customer in order to reduce customers’ search effort, to reflect frequently changing trends by emphasizing the important items and to improve the rate of recommendation with high purchasability. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.