• International Area Studies Review
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• v.15 no.3
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• pp.145-174
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• 2011

Attracting foreign direct investment in the mining sector becomes a key factor for the continuing economic growth for mining-dependent developing countries. This paper attempts to identify the determining factors that attract FDI inflows into the mining sector. Based on previous conceptual studies, the authors have attempted empirical analyses on a panel of 40 mining countries for the period 1996-2009. These empirical results are the first of their kind given the variables employed are arguably the most comprehensive and exhaustive to date. The empirical results show that market size, trade openness, quality of mined products, quality of infrastructure, regulatory quality, and perceived economic risk associated with the country are positively related to investments in mining. Whereas, tariff rate, corporate tax rate, extent of corruption, and political instability are negatively related to FDI inflows in the mining sector. The empirical results also show that developing countries tend to attract greater amounts of FDI in the mining sector compared to their developed counterparts.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.255-268
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• 2019

Taking top-coal caving mining face (TCCMF) as research object, this paper considers the combination of top-coal and immediate roof as cushion layer to build the solution model of support resistance based on the theory of elastic foundation beam. Meanwhile, the physical and mechanical properties of coal-rock combination influencing on strata behaviors is explored. The results illustrate that the subsidence of main roof in coal wall increases and the first weighting interval decreases with the increase of top-coal and immediate roof thicknesses as well as the decrease of top-coal and immediate roof elastic modulus. Moreover, the overlying strata reflecting on support has negative and positive relationship with top-coal thickness and immediate roof thickness, respectively. However, elastic modulus has limit influence on the dead weight of top-coal and immediate roof. As a result, it has similar roles on the increase of total support resistance and overlying strata reflecting on support in the limit range of roof control distance. In view of sensitive analysis causing the change of total support resistance, it can be regards as the rank of three components as immediate roof weight > overlying strata reflecting on support > top coal weight. Finally, combined with the monitoring data of support resistance in Qingdong 828, the validity of support resistance determined based on elastic foundation beam is demonstrated, and this method can be recommended to adopt for support type selecting in TCCMF.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.1
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• pp.217-231
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• 2015

This study aims to infer the gene-protein 'brings_about' chains of pancreatic cancer which were referred to in the pancreatic cancer related researches by constructing the gene-protein interaction network of pancreatic cancer. The chains can help us uncover publicly unknown knowledge that would develop as empirical studies for investigating the cause of pancreatic cancer. In this study, we applied a novel approach that grafts text mining and the main path analysis into Swanson's ABC model for expanding intermediate concepts to multi-levels and extracting the most significant path. We carried out text mining analysis on the full texts of the pancreatic cancer research papers published during the last ten-year period and extracted the gene-protein entities and relations. The 'brings_about' network was established with bio relations represented by bio verbs. We also applied main path analysis to the network. We found the main direct 'brings_about' path of pancreatic cancer which includes 14 nodes and 13 arcs. 9 arcs were confirmed as the actual relations emerged on the related researches while the other 4 arcs were arisen in the network transformation process for main path analysis. We believe that our approach to combining text mining analysis with main path analysis can be a useful tool for inferring undiscovered knowledge in the situation where either a starting or an ending point is unknown.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.531-541
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• 2001

In this study, we compare the capabilities of the data mining tools of the most updated version objectively and provide the useful information in which enterprises and universities chose them. In particular, we compare the SAS/Enterprise Miner 3.0, SPSS/Clementine 5.2 and IBM/Intelligent Miner 6.1 which are well known and easily gotten.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.937-943
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• 2000

Data mining usually deal with undesigned massive data containing many variables for which their characteristics and association rules are unknown, therefore it is actually not easy to interpret the results of analysis. In this paper, it is shown that decision tree can be very useful in interpreting data mining prediction model using two real examples.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.601-605
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• 2009

The objective of this research is to develop a knowledge discovery framework which can help project teams discover useful patterns to improve energy efficient building design. This paper utilizes the technology of data mining to automatically extract concepts, interrelationships and patterns of interest from a large dataset. By applying data mining technology to the analysis of energy efficient building designs one can identify valid, useful, and previously unknown patterns of energy simulation modeling.

• Ocean and Polar Research
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• v.36 no.4
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• pp.475-485
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• 2014

Seawater flow characteristics around a manganese nodule mining device in deep sea were analyzed through numerical investigation. The mining device influences the seawater flow field with complicated velocity distributions, and they are largely dependent on the seawater flow speed, device moving speed, and injection velocity from the collecting part. The flow velocity and turbulent kinetic energy distributions are compared at several positions from the device rear, side, and top, and it is possible to predict the distance from which the mining device affects the seawater flow field through the variation of turbulent kinetic energy. With the operation of the collecting device the turbulent kinetic energy remarkably increases, and it gradually decreases along the seawater flow direction. Turbulent kinetic energy behind the mining system increases with the seawater flow velocity. The transient behavior of nodule particles, which are not collected, is also predicted. This study will be helpful in creating an optimal design for a manganese nodule collecting device that can operate efficiently and which is eco-friendly.

• International Journal of Contents
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• v.3 no.2
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• pp.18-24
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• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.