• Title/Summary/Keyword: Mining

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A Novel Approach for Mining High-Utility Sequential Patterns in Sequence Databases

  • Ahmed, Chowdhury Farhan;Tanbeer, Syed Khairuzzaman;Jeong, Byeong-Soo
    • ETRI Journal
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    • v.32 no.5
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    • pp.676-686
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    • 2010
  • Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

Influence of explosives distribution on coal fragmentation in top-coal caving mining

  • Liu, Fei;Silva, Jhon;Yang, Shengli;Lv, Huayong;Zhang, Jinwang
    • Geomechanics and Engineering
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    • v.18 no.2
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    • pp.111-119
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    • 2019
  • Due to certain geological characteristics (high thickness, rocky properties), some underground coal mines require the use of explosives. This paper explores the effects of fragmentation of different decks detonated simultaneously in a single borehole with the use of numerical analysis. ANSYS/LS-DYNA code was used for the implementation of the models. The models include an erosion criterion to simulate the cracks generated by the explosion. As expected, the near-borehole area was damaged by compression stresses, while far zones and the free surface of the boundary were subjected to tensile damage. With the increase of the number of decks in the borehole, different changes in the fracture pattern were observed, and the superposition effects of the stress wave became evident, affecting the fragmentation results. The superposition effect is more evident in close distances to the borehole, and its effect attenuates when the distance to the borehole increase.

Feature Selection Methodology in Quality Data Mining

  • Soo, Nam-Ho;Halim, Yulius
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2004.05a
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    • pp.698-701
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    • 2004
  • In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.

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Interplay of Text Mining and Data Mining for Classifying Web Contents (웹 컨텐츠의 분류를 위한 텍스트마이닝과 데이터마이닝의 통합 방법 연구)

  • 최윤정;박승수
    • Korean Journal of Cognitive Science
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    • v.13 no.3
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    • pp.33-46
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    • 2002
  • Recently, unstructured random data such as website logs, texts and tables etc, have been flooding in the internet. Among these unstructured data there are potentially very useful data such as bulletin boards and e-mails that are used for customer services and the output from search engines. Various text mining tools have been introduced to deal with those data. But most of them lack accuracy compared to traditional data mining tools that deal with structured data. Hence, it has been sought to find a way to apply data mining techniques to these text data. In this paper, we propose a text mining system which can incooperate existing data mining methods. We use text mining as a preprocessing tool to generate formatted data to be used as input to the data mining system. The output of the data mining system is used as feedback data to the text mining to guide further categorization. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We apply this method to categorize web sites containing adult contents as well as illegal contents. The result shows improvements in categorization performance for previously ambiguous data.

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A Quality Data Mining System in TFT-LCD Industry (TFT-LCD 산업에서의 품질마이닝 시스템)

  • Lee, Hyun-Woo;Nam, Ho-Soo
    • Journal of Korean Society for Quality Management
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    • v.34 no.1
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    • pp.13-19
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    • 2006
  • Data mining is a useful tool for analyzing data from different perspectives and for summarizing them into useful information. Recently, the data mining methods are applied to solving quality problems of the manufacturing processes. This paper discusses the problems of construction of a quality mining system, which is based on the various data mining methods. The quality mining system includes recipe optimization, significant difference test, finding critical processes, forecasting the yield. The contents and system of this paper are focused on the TFT-LCD manufacturing process. We also provide some illustrative field examples of the quality mining system.

A STUDY ON THE SYSTEM DEVELOPMENT FOR MANAGEMENT OF MINING-RELATED DAMAGES USING GIS

  • Kim, Jung-A;Yoon, Suk-Ho;Kim, Won-Kyun;Choi, Jong-Kuk
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.95-97
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    • 2007
  • The mining-related damages due to the mining operations such as ground subsidence, tailing, Acid Mine Drainage, and soil contamination have a significant effect on our social and economical environment. So, for the effective prevention and reclamation works of the hazards in the mining area, the systematic management of mine information and mining-related damages is urgently needed. In this study, we estimated the possibilities of GIS-based system development for the mining area and related database. We classified the steps of building GIS as mine itself, mining-related damages, rehabilitation works and additional functions for estimating damages and analyzed the essential database and functions for each step. GIS will be helpful to estimate the mining-related damages and to carry out the reclamation works effectively.

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Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism (하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출)

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.764-770
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    • 2004
  • This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

Using Ontologies for Semantic Text Mining (시맨틱 텍스트 마이닝을 위한 온톨로지 활용 방안)

  • Yu, Eun-Ji;Kim, Jung-Chul;Lee, Choon-Youl;Kim, Nam-Gyu
    • 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.

Experimental investigation on multi-parameter classification predicting degradation model for rock failure using Bayesian method

  • Wang, Chunlai;Li, Changfeng;Chen, Zeng;Liao, Zefeng;Zhao, Guangming;Shi, Feng;Yu, Weijian
    • Geomechanics and Engineering
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    • v.20 no.2
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    • pp.113-120
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    • 2020
  • Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

Fuzzy Web Usage Mining for User Modeling

  • Jang, Jae-Sung;Jun, Sung-Hae;Oh, Kyung-Whan
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.3
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    • pp.204-209
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    • 2002
  • The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.