• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.695-697
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• 2003

다중데이터베이스 마이닝에서 하나의 데이터 집합을 형성하는 작업은 많은 부하가 따른다. 그러므로, 본 논문에서는, 가중치 거리를 이용한 클러스터링을 통해 관련성이 높은 데이터베이스를 식별하는 기법을 제안한다. 제안한 기법은 빈발한 항목으로 구성된 데이터 집합을 생성하여 데이터베이스 사이의 유사성과 거리를 측정하고 데이터베이스간의 거리에 대한 식별성을 향상시키기 위하여 최다 빈발항목에 대한 비교 연산을 통해 가중치를 산출한다. 그리고 성능평가를 통하여 제안한 기법이 Ideal&Goodness 기법보다 다중데이터베이스의 트랜잭션 데이터베이스에 대한 식별 능력이 우수함을 알 수 있었다.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.285-291
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• 2010

In this paper, we propose a new paradigm of weighting methods for naive Bayesian learning. We propose more fine-grained weighting methods, called value weighting method, in the context of naive Bayesian learning. While the current weighting methods assign a weight to an attribute, we assign a weight to an attribute value. We develop new methods, using Kullback-Leibler function, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general naive bayesian. The proposed method shows better performance in most of the cases.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.415-420
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• 2022

It is essential that continual assessments of the impact of mine-derived water as a long-lasting burden on freshwater environments. Abundance-based evaluations of benthic macroinvertebrates have been conducted to evaluate anthropogenic disturbances and devise policies to reduce their impact. In this study, the status of a stream habitat was evaluated based on the body length and biomass weight of benthic macroinvertebrates of the family Baetidae. Following the renewal of the mining water treatment plant, the abundance of Baetidae assemblages recovered to a level comparable to that of a reference site. However, relatively low values were found for both body length and biomass weight in Baetidae species inhabiting the reddened streambed area, suggesting that the habitat has not yet been completely recovered despite the recovery of the abundance of the Baetidae assemblages. Therefore, continuous investigation and evaluation of this disturbed stream are necessary until their growth conditions of the habitat have functionally recovered.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.381-388
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• 2010

Naive Bayesian learning has been widely used in many data mining applications, and it performs surprisingly well on many applications. However, due to the assumption that all attributes are equally important in naive Bayesian learning, the posterior probabilities estimated by naive Bayesian are sometimes poor. In this paper, we propose more fine-grained weighting methods, called value weighting, in the context of naive Bayesian learning. While the current weighting methods assign a weight to each attribute, we assign a weight to each attribute value. We investigate how the proposed value weighting effects the performance of naive Bayesian learning. We develop new methods, using gradient descent method, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general Naive bayesian, and the value weighting method showed better in most cases.

• Tunnel and Underground Space
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• v.24 no.4
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• pp.282-288
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• 2014

Stoping method has been acknowledged as one of the typical metalliferous underground mining methods. Notwithstanding with the popularity of the method, the majority of stoping mines are suffering from excessive unplanned dilution which often becomes as the main cause of mine closure. Thus a reliable unplanned dilution management system is imperatively needed. In this study, reliable unplanned dilution prediction system is introduced by adopting artificial neural network (ANN) based on data investigated from one underground stoping mine in Western Australia. In addition, contributions of input parameters were analysed by connection weight algorithm (CWA). To validate the reliability of the proposed ANN, correlation coefficient (R) was calculated in the training and test stage which shown relatively high correlation of 0.9641 in training and 0.7933 in test stage. As results of CWA application, BHL (Length of blast hole) and SFJ (Safety factor of Joint orientation) show comparatively high contribution of 18.78% and 19.77% which imply that these are somewhat critical influential parameter of unplanned dilution.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.61-69
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• 2015

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.177-187
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• 2002

Spatial data mining is a process to discover interesting relationships and characteristics those exist implicitly in a spatial database. Many spatial clustering algorithms have been developed. But, there are few approaches that focus simultaneously on clustering spatial data and assigning weight to non-spatial attributes of objects. In this paper, we propose a new spatial clustering algorithm, called DBSCAN-W, which is an extension of the existing density-based clustering algorithm DBSCAN. DBSCAN algorithm considers only the location of objects for clustering objects, whereas DBSCAN-W considers not only the location of each object but also its non-spatial attributes relevant to a given application. In DBSCAN-W, each datum has a region represented as a circle of various radius, where the radius means the degree of the importance of the object in the application. We showed that DBSCAN-W is effective in generating clusters reflecting the users requirements through experiments.

• Geomechanics and Engineering
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• v.23 no.2
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.471-477
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• 2018

As time changes, users change their interest. In this paper, we propose a method to provide suitable service for users by dynamically weighting service interests in the context of age, timing, and seasonal changes in ubiquitous environment. Based on the service history data presented to users according to the age or season, we also offer useful services by continuously adding the most recent service rules to reflect the changing of service interest. To do this, a set of services is considered as a transaction and each service is considered as an item in a transaction. And also we represent the association of services in a graph and extract frequent service items that refer to the latest information services for users.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.