• Geomechanics and Engineering
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• v.23 no.1
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• pp.61-69
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• 2020

The uniaxial compressive strength (UCS) of rock is a basic parameter in underground engineering design. The disadvantages of this commonly employed laboratory testing method are untimely testing, difficulty in performing core testing of broken rock mass and long and complicated onsite testing processes. Therefore, the development of a fast and simple in situ rock UCS testing method for field use is urgent. In this study, a multi-function digital rock drilling and testing system and a digital core bit dedicated to the system are independently developed and employed in digital drilling tests on rock specimens with different strengths. The energy analysis is performed during rock cutting to estimate the energy consumed by the drill bit to remove a unit volume of rock. Two quantitative relationship models of energy analysis-based core drilling parameters (ECD) and rock UCS (ECD-UCS models) are established in this manuscript by the methods of regression analysis and support vector machine (SVM). The predictive abilities of the two models are comparatively analysed. The results show that the mean value of relative difference between the predicted rock UCS values and the UCS values measured by the laboratory uniaxial compression test in the prediction set are 3.76 MPa and 4.30 MPa, respectively, and the standard deviations are 2.08 MPa and 4.14 MPa, respectively. The regression analysis-based ECD-UCS model has a more stable predictive ability. The energy analysis-based rock drilling method for the prediction of UCS is proposed. This method realized the quick and convenient in situ test of rock UCS.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.137-144
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• 2013

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.45-55
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• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• ETRI Journal
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• v.37 no.2
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• pp.317-327
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• 2015

A reliable analysis of consumer preference from a large amount of purchase data acquired in real time and an accurate customer characterization technique are essential for successful direct marketing campaigns. In this study, an optimal segmentation of post office customers in Korea is performed using a subspace projection-based clustering method to generate an accurate customer characterization from a high-dimensional census dataset. Moreover, a traditional temporal mining method is extended to an algorithm using the MapReduce framework for a consumer preference analysis. The experimental results show that it is possible to use parallel mining through a MapReduce-based algorithm and that the execution time of the algorithm is faster than that of a traditional method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1739-1748
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• 2008

When we conduct a data mining procedure on sample data sources, several rules are generated. But some rules are redundant or logically disjoint and therefore they can be removed. We suggest a new rule minimization algorithm inspired from logic synthesis to improve comprehensibility and eliminate redundant rules. The method can merge several relevant rules into one based on data mining and logic synthesis without high loss of accuracy. In case of two or more rules are candidates to be merged, we merge the rules with the attribute having the lowest information gain. To show the proposed method could be a reasonable solution, we applied the proposed approach to a problem domain constructing user preferred ontology in anti-spam systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.94-101
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• 2005

This paper presents a data-mining aided heuristic algorithm development. The developed algorithm includes three steps. The steps are a uniform selection, development of feature functions and clustering, and a decision tree making. The developed algorithm is employed in designing an optimal multi-station fixture layout. The objective is to minimize the sensitivity function subject to geometric constraints. Its benefit is presented by a comparison with currently available optimization methods.

• East Asian mathematical journal
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• v.35 no.4
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• pp.489-508
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• 2019

In this paper we used text mining method to analyze journals of mathematics education posterior to the year of 2016. To figure out trends of mathematics education research. we analyzed the key words largely mentioned in the recent mathematics education journals by Term Frequency and Term Frequency-Inverse Document Frequency method. We also looked at how these keywords match up with the key words that appear of education to prepare for future society. This result can infer the characteristics of mathematics education research in the aspect upcoming research topics.

• The Journal of Information Systems
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• v.21 no.3
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• pp.137-161
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• 2012

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.