• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.177-187
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• 2013

Association rule mining is a popular data mining technique with a wide range of application domains, and aims to extract the cause-and-effect relations between the discrete items included in transaction data. However, analysts sometimes have trouble in interpreting and using the plethora of association rules extracted from a large amount of data. To address this problem, this paper aims to propose a novel approach called HTM for extracting the interesting association rules from given transaction data. The HTM approach consists of three main steps, hierarchical clustering, table-view, and mosaic plot, and each step provides the analysts with appropriate visual representation. For illustration, we applied our approach for analyzing the mass health examination data, and the result of this experiment reveals that the HTM approach help the analysts to find the interesting association rules in more effective way.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.495-503
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• 2011

The most widely used data mining technique is to find association rules. Association rule mining is the method to quantify the relationship between each set of items in very huge database based on the association thresholds. There are some basic association thresholds to explore meaningful association rules ; support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence and the attributably pure confidence were developed to compensate for this drawback, but they have other drawbacks.In this paper we consider some predictive similarity measures for binary data in cluster analysis and multi-dimensional analysis as association threshold to compensate for these drawbacks. The comparative studies with net confidence, attributably pure confidence, and some predictive similarity measures are shown by numerical example.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.671-678
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• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.53-64
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• 2002

Association rule discovery is a method that detects associative relationships between items or attributes in transactions. It is one of the most widely studied problems in data mining because it offers useful insight into the types of dependencies that exist in a data set. However, most studies on association rule discovery have the drawback that they can not discover association rules among user groups that have common characteristics. To solve this problem, we segment the set of users into user-subgroups by using feature selection and the user segmentation, thus local association rules in user-subgroup can be discovered. To evaluate that the local association rules are more appropriated than the global association rules in each user-subgroup, derived local association rules are compared with global association rules in terms of several evaluation measures.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.81-88
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• 2020

Various methods of differentiating and servicing digital content for individual users have been studied. Searching for association rules is a very useful way to discover individual preferences in digital content services. The Apriori algorithm is useful as an association rule extractor using frequent itemsets. However, the Apriori algorithm is not suitable for application to an actual content service because it considers only the reference count of each content. In this paper, we propose a new algorithm based on the Apriori that searches association rules by using purchase history and usage-time for each item. The proposed algorithm utilizes the usage time with the weight value according to purchase items. Thus, it is possible to extract the exact preference of the actual user. We implement the proposed algorithm and verify the performance through the actual data presented in the actual content service system.

• (The)Study of the Eastern Classic
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• no.68
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• pp.509-538
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• 2017

After 20thcentury, digital game has placed very important aspect in modern culture and industries. Thus, digital game industries have utilized ancient stories for themes and ideas. This trial is not only spotted in industrial sectors but also in liberal arts. This paper has a purpose to look at factors that can convert components from classic novels into digital games. Especially, it will focus on how researchers of classical literature can affect in different industrial sectors. Current analysis shows that stories, fantasies, items of classic novel can essentially help to create new games. However, a game consists of just more than the previous stated components; it requires rules, outcome, conflicts and voluntary to make the games much more interesting. It is often misunderstood that plot and fantasies in games may be the most significant aspects but they are actually not. Classic novel can help to make the rule of the four standards in creating a fun game. Here are rules that Taewonji suggests. First, gamers need to save the bleak world from a turbulent age. Second, The will of the absolute has the ultimate power, so those who have achieved god's will can raise a new country. Thirdly, the sinocentrism and the outskirts of the country are not imperative but they are adjustable notions. The first and second aspects are commonly found in Samkukjiyeonui. Hence, the pattern is very familiar. The most significant rule is the third determinant since the idea purely came from Taewonji. The main character Im Seong from the story was the center of his home country, but he later becomes the outskirt or the barbarian of the new country. Therefore, the players should maintain the three rules when they are following the plot and using different items in the digital game world. The researchers of classic literature should find rules that are suitable for the game from the classic novels. This way, there is no need to discover a separate field for digital games out of classical literature.

• Journal of the Korean School Mathematics Society
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• v.5 no.2
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• pp.1-15
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• 2002

The purpose of this study came to start to be helpful in that the teachers in field produced evaluation subject about the mathematics and applied by analyzing the performance capacity for the students about the subject developed through the application of subject about the teaching-learning model that can utilize the learners' learning experience and the development of items that can evaluate it by utilizing various and concrete operational materials as an object of the first year at the middle schools. The concrete purpose of realizing it is as follows. 1. This study is to develop the teaching-learning model centered on activities adequate to utilize various and concrete operational materials. 2. This study is to develop evaluation data for utilizing the concrete operational materials by making construction through the application of developed teaching-learning model as possible. 3. This study is to present standards initiative for reliable and fair marking about the evaluation data and to analyze the students' practice capacity by using it effectively. For accomplishing the purpose of this research, this study is the work-oriented teaching and learning model using learning data that can be easily found around the surroundings and 8 evaluation programs in order that the experience-oriented learning based on circumstances learning among the learning models of constructivism. Also, it is to examine the result after applying that on a basis of 25 students, consisted of only on class in a year, in the first year at the small size of agricultural middle schools. The result of this study from this research is as follows. First, as a result of frequency survey, of response about the developed evaluation data it showed positive response more than 80% of all the items. The atmosphere of self-directed learning was produced because the developed evaluation data could induce activity-based learning naturally by stimulating the students' curiosity and promoting interest. Second, this study executed t-test for the result of questionnaire about the mathematics propensity, and there were significant differences in 19 items among 25 items. It presented that the application of data about the teaching-learning and evaluation directly using concrete operational materials to the constructional learning by level might have desirable affect on the students' mathematical propensity. It came to be a motive that could increase value recognition and interest on the subject of mathematics by investigating a mathematical principle rule and confirming it through activity learning using the concrete operational materials.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.10
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• pp.1595-1607
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• 2008

The purpose of this study is to find the reason why the National Mourning Dress System of the Joseon Dynasty had been established although the General Mourning Dress System had exist through research of three things; differences of each social class, changes of the National Mourning Dress System by the age and differences between the National one and the General one. The methodology which is adopted for this study is comparative analysis by social class, period and age from the Annals of the Joseon Dynasty and the Orye-e. And this study is focus on the women's dress. The results of this study is as follows; Firstly, some mourning dress items had been different or not used by class. Secondly, although there had not been a great change, some dress items had been disappeared or used irregularly. And Naemyoungbu Naekwan's mourning dress had been changed by the age. Thirdly, The National one was more extensive in wearing and applied differently by class compare to the General one. Also it had an additional rule according to the period, duty and place to Naemyoungbu-Naekwan and Naemyoungbu-Goongkwan. The National one had been established by ruling class of The Joseon Dynasty who had tried to keep the Confucian standard and perfect social status system. And it is supposed that the National Mourning Dress System had not been a great change because the ruling class' will had not been changed.

• Proceedings of the KSPS conference
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• 1996.02a
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• pp.137-155
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• 1996

The purpose of this paper is to generate a pitch contour which is affected by tile phonetic environment and the number of syllables in each Korean isolated word using a neural network. To do this, we analyzed a set of 513 Korean isolated words, consisting of 1-4 syllables and extracted the pitch contour and the duration of each phoneme in all the words. The total number of phonemes we analyzed is about 3800. After that we approximated the pitch contour with a 1st order polynominal by a regression analysis. We could get the slope, the initial pitch and the duration of each phoneme. We used these 3 parameters as the target pattern of the neural network and let the neural network learn the rule of the variation of the pitch and duration, which was affected by the phonetic environment of each phoneme. We used 7 consecutive phoneme strings as an input pattern for a neural network to make the network learn the effect of phonetic environment around the center phoneme. In the learning phase, we used 3545 items(463 words) as target patterns which contained the phonetic environment of front and rear 3 phonemes and the neural network showed the correctness rate of 98.43%, 98.59%, 97.7% in the estimation of the duration, the slope, the initial pitch. In the recall phase, we tested the performance of tile neural network with 251 items(50 words) which weren't need as learning data and we could get the good correctness rate of 97.34%, 95.45%, 96.3% in the generation of the duration, the slope, and the initial pitch of each phoneme.