• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.625-632
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• 2001

The electronic stores have realized that they need to understand their customers and to quickly response their wants and needs. To be successful in increasingly competitive Internet marketplace, recommender systems are adapting data mining techniques. One of most successful recommender technologies is collaborative filtering (CF) algorithm which recommends products to a target customer based on the information of other customers and employ statistical techniques to find a set of customers known as neighbors. However, the application of the systems, however, is not very suitable for seasonal products which are sensitive to time or season such as refrigerator or seasonal clothes. In this paper, we propose a new adjusted item-based recommendation generation algorithms called the exponentially weighted collaborative filtering recommendation (EWCFR) one that computes item-item similarities regarding seasonal products. Finally, we suggest the recommendation system with relatively high quality computing time on main memory database (MMDB) in XML since the collaborative filtering systems are needed that can quickly produce high quality recommendations with very large-scale problems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.1-15
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• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.111-121
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• 2006

As Internet lastly grows, network attack techniques are transformed and new attack types are appearing. The existing network-based intrusion detection systems detect well known attack, but the false-positive or false-negative against unknown attack is appearing high. In addition, The existing network-based intrusion detection systems is difficult to real time detection against a large network pack data in the network and to response and recognition against new attack type. Therefore, it requires method to heighten the detection rate about a various large dataset and to reduce the false-positive. In this paper, we propose method to reduce the false-positive using multi-level detection algorithm, that is combine the multidimensional Apriori algorithm and the modified Negative Selection algorithm. And we apply this algorithm in intrusion detection and, to be sure, it has a good performance.

• The Journal of Internal Korean Medicine
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• v.44 no.1
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• pp.35-52
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• 2023

Objective: Internal injuries and consumptive disease have different causes, yet they can affect each other. The relationship and combination of prescription drugs in the clinical practice of internal injuries and consumptive disease were analyzed for various diseases of "Donguibogam" through network analysis. Methods: The prescriptions used in consumptive disease and internal injury were established by conducting a full survey on the papers extracted from Donguibogam. The R version 4.0.3 (2020-10-10) and the igraph and arules package were used to perform network analysis and association rule relationship mining analysis in the first and second prescription compositions. Results: The herb frequently used for internal injury was Glycyrrhizae Radix, while the herb combination frequently used was Citri Pericarpium-Glycyrrhizae Radix. For centrality, the main factor was generally Glycyrrhizae Radix. In the case of consumptive disease, the herb most frequently used was Angelicae Gigantis Radix, and the combination most frequently used was Rehmanniae Radix Preparata-Angelicae Gigantis Radix. In terms of centrality, it was Angelicae Gigantis Radix. As a result of the network analysis of herbal prescription frequency, each group was divided into three. Conclusion: The interrelationship between internal injury and consumptive disease prescription drugs may reveal the differences and similarities between internal injury and consumptive disease and may serve as a basis for the development of new drugs or materials that can enhance mutual effectiveness in the treatment of internal injury and consumptive diseases.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Information Systems Review
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• v.15 no.3
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• pp.1-37
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• 2013

This study is conducted to explore the possibility of service convergence to promote mobile learning. This study has attempted to identify how mobile learning service is provided, which services among them are considered most popular, and which services are highly demanded by users. This study has also investigated the potential opportunities for service convergence of mobile service and e-learning. This research is then extended to examine the possibility of active convergence of common services in mobile services and e-learning. Important variables have been identified from related web pages of portal sites using social network analysis (SNA) and association rules. Due to the differences in number and type of variables on different web pages, SNA was used to deal with the difficulties of identifying the degree of complex connection. Association analysis has been used to identify association rules among variables. The study has revealed that most frequent services among common services of mobile services and e-learning were Games and SNS followed by Payment, Advertising, Mail, Event, Animation, Cloud, e-Book, Augmented Reality and Jobs. This study has also found that Search, News, GPS in mobile services were turned out to be very highly demanded while Simulation, Culture, Public Education were highly demanded in e-learning. In addition, It has been found that variables involving with high service convergence based on common variables of mobile and e-learning services were Games and SNS, Games and Sports, SNS and Advertising, Games and Event, SNS and e-Book, Games and Community in mobile services while Games, Animation, Counseling, e-Book, being preceding services Simulation, Speaking, Public Education, Attendance Management were turned out be highly convergent in e-learning services. Finally, this study has attempted to predict possibility of active service convergence focusing on Games, SNS, e-Book which were highly demanded common services in mobile and e-learning services. It is expected that this study can be used to suggest a strategic direction to promote mobile learning by converging mobile services and e-learning.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2907-2915
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• 2018

Journal of the Korean Data Analysis Society founded in 1998 has played the role of a major application journal. In this study, we checked the objective of this journal by checking the abstracts for 10 years. Abstract data was crawled from the online journal site (kdas.jems.or.kr) and analyzed by topic model. As a result, we found 18 topics from 2680 abstracts that had several contents, for example, nursing, marketing, economics, regression, factor analysis, data mining and statistical inferences. Topic1 (regression) is most frequent with 460 documents and we found the usefulness of regression in the applied science area. We confirmed the significant 10 association rules using by Fisher's exact test. Also, for exploring the trend of topics, we conducted the topic analysis for two periods which are 2006-2011 period and 2012-2016 period. We found that the control study was more frequent than survey study over time and regression and factor analysis were frequent regardless of time.

• Korean Journal of Social Welfare
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• v.60 no.1
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• pp.187-209
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• 2008

The authors developed 28 needs assessment tools for integrated assessment centered on needs, which is the core element in care management for the elderly in home. Also, the authors collected the assessment data of 676 elderly persons in home from 120 centers under the Korea Association of Senior Welfare Centers by using the needs assessment tools, and finally developed needs extraction algorithm through decision tree analysis in data mining to identify their actual needs and provide social welfare service suitable for such needs. The needs extraction algorithm for 28 needs of the elderly in home are summarized in