• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.285-290
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• 2009

An association rule mining has been studied to find hidden data pattern in data mining. A realization of fast processing method have became a big issue because it treated a great number of transaction data. The time which is derived by association rule finding method geometrically increase according to a number of item included data. Accordingly, the process to reduce the number of rules is necessarily needed. We propose the T-algorithm that is efficient rule reduction algorithm. The T-algorithm can reduce effectively the number of association rules. Because that the T-algorithm compares transaction data item with binary format. And improves a support and a confidence between items. The performance of the proposed T-algorithm is evaluated from a simulation.

• Journal of Applied Reliability
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• v.4 no.1
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• pp.15-26
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• 2004

Life test is performed to set a confidence (lower) limit on the mean or median life of items if the number of failures at the end of the fixed time t does not exceed a given number c. Gupta(1962) propose a sampling plan for truncated life tests when the life distribution of an item is normal or lognormal distribution. In this paper, based on the result of Gupta(1962), we propose a sampling plan for failure rate test when an item has normal or lognormal life distribution. We assume that the shape parameter is known while the location parameter is unknown.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.20
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• pp.39-45
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• 1989

This article is concerned with cost analysis in product warranty policy. The warranty cost can be different according to warranty rate and warranty renewal policy. In this paper the stepdown warranty is used. The warranty renewal policy is considered when the warranty is received upon free replacement period as item failing. Assuming the non repairable item as one item is sold, investigated manufacturer's cost in stepdown warranty policy. Also manufacturer's cost is calculated in the free replacement. pro-rata. hybrid policy. Numerical example is given over Weibull time to failure distribution, comparing stepdown warranty policy with free replacement, pro-rata, hybrid one in the manufacturer's point of view. The sensitivity analysis of warranty cost according to the number of warranty period step is included.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.137-153
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• 2005

Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.185-198
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• 2002

In the traditional life testing model, it is assumed that a certain number of identical items are tested under identical condition. This is due to statistical rather than practical considerations. The proportional hazards model can be used to develop a realistic approach to determine the performance of an item. That is also capable of modeling the failure rates of accelerated life testing when the covariates are applied stresses. The proportional hazards model is typically applied for a group of items to assess the importance of factors that may influence the reliability of an item. In this paper we considered the interarrival times of an item rather than the time to first failure for grouped items and provided the availability estimation for the determination of maintenance policy and overhaul time. In order to demonstrate the proposed approach, an example is presented.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.238-246
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• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

• KSCI Review
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• v.8 no.2
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• pp.33-42
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• 2001

The purpose of this study was to help retrieve and assess only what learner wants. The multiple-choice and short-answer types were selected. and a sort of a question bank was organized in consideration of the degree of difficulty and frequency of being questioned in such a way to have a discriminating power. For item retrieval the stored information was converted into XML data, instead of simply searching information from database. and that data were retrieved through Xpath. And it's designed to show the retrieval output by using XML on browser. Concerning item evaluation. evaluation items were produced by inputting the degree of difficulty and frequency of being questioned of the subject and unit learner wants. and then by inputting the number of individual item type. The learning outcome was offered in real time to learner. and learner could repeatedly drill what they gave a wrong answer.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.686-711
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• 2015

The performance of a novel medical question-answering engine called CliniCluster and existing search engines, such as CQA-1.0, Google, and Google Scholar, was evaluated using known-item searching. Known-item searching is a document that has been critically appraised to be highly relevant to a therapy question. Results show that, using CliniCluster, known-items were retrieved on average at rank 2 ($MRR@10{\approx}0.50$), and most of the known-items could be identified from the top-10 document lists. In response to ill-defined questions, the known-items were ranked lower by CliniCluster and CQA-1.0, whereas for Google and Google Scholar, significant difference in ranking was not found between well- and ill-defined questions. Less than 40% of the known-items could be identified from the top-10 documents retrieved by CQA-1.0, Google, and Google Scholar. An analysis of the top-ranked documents by strength of evidence revealed that CliniCluster outperformed other search engines by providing a higher number of recent publications with the highest study design. In conclusion, the overall results support the use of CliniCluster in answering therapy questions by ranking highly relevant documents in the top positions of the search results.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.1-10
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• 2015

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.127-133
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• 2007

The u-learning environment has been developed through a number of iterations, and has now been formally evaluated, through analysis of student learning results and the use of quantitative and qualitative measures, Generally, for advance learning effect and analysis of student learning results, the most learning system be use to the item analysis method. But, nowadays, it has using the IRT(Item Response Theory) instead of the item analysis method, The IRT adopts explicit models for the probability of each possible response to a test. Therefore, I proposed the lightweight component based u-learning system using the IRT. Applied device of u-learning is PDA which is in Windows mobile 5.0 environments.