• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.

• 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.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.445-452
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• 2018

In a world where everything in life is being digitized, the amount of data is increasing exponentially. These data are processed into new data through collection and analysis. New data is used for a variety of purposes in hospitals, finance, and businesses. However, since existing data contains sensitive information of individuals, there is a fear of personal privacy exposure during collection and analysis. As a solution, there is privacy-preserving data mining (PPDM) technology. PPDM is a method of extracting useful information from data while preserving privacy. In this paper, we investigate PPDM and analyze various measures for evaluating the privacy and utility of data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.716-718
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• 2004

The false alarm data in intrusion detection systems are divided into false positive and false negative. The false positive makes bad effects on the performance of intrusion detection system. And the false negative makes bad effects on the efficiency of intrusion detection system. Recently, the most of works have been studied the data mining technique for analysis of alert data. However, the false alarm data not only increase data volume but also change patterns of alert data along the time line. Therefore, we need a tool that can analyze patterns that change characteristics when we look for new patterns. In this paper, we focus on the false positives and present a framework for analysis of false alarm pattern from the alert data. In this work, we also apply incremental data mining techniques to analyze patterns of false alarms among alert data that are incremental over the time. Finally, we achieved flexibility by using dynamic support threshold, because the volume of alert data as well as included false alarms increases irregular.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.1-8
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• 2010

This study tested the applicability of a Data mining tool in the analysis of massive National Pension data for the purpose of developing fraud pension payment prediction model. This study is identified significant variables for fraud pension payment through the statistical analysis process and developed prediction models using data mining methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4133-4145
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• 2017

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

• Journal of the Korean Home Economics Association
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• v.43 no.3 s.205
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• pp.1-10
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• 2005

The purpose of this study was to investigate the enforcement strategy for Consumer Service marketing of an e-Commerce shopping mall. An e-CRM for a Cosmetic e-Commerce shopping mall, Data Warehousing(DW) component, analysis of data mining of the DW, and web applications and strategies had to developed for marketing of consumer service satisfaction. The major findings were as follows: An RFM analysis was used for consumer classification, which is a fundamental process of e-CRM application. The components of the DW were web sales data and consumer data fields. The visual process of consumer segmentations (superior consumer class) for e-CRM solutions is presented. The association analysis algorithm of data mining to up-selling and cross-selling indicates an association rule. These e-CRM results apply web DB marketing and operating principles to a shopping mall. Therefore, the system applications of e-CRM to Consumer services indicate a marketing strategy for consumer-oriented management.

• Journal of Engineering Education Research
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• v.9 no.4
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• pp.71-82
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• 2006

To accomplish the educational objectives of a department, a system for CQI(Continuous Quality Improvement) is necessary. Improving the educational system by survey analysis is one of the most important factors for accomplishing the educational objectives. In general, survey analysis is carried out by using statistical distribution on an attribute or correlation analysis between two attributes. However, these analysis schemes have a limitation that they cannot find relations among various attributes. In this paper, an in-depth survey analysis method applying data mining techniques is presented. Data mining is a technique for extracting interesting knowledges from a large set of data. Survey from undergraduate students in the School of Computing of Soongsil University is analyzed in this paper by using a data mining tool, called Clementine. Results of Clementine analysis show the relationship between 'grade', and other attributes hierarchically, and provide useful information that can be applied in student consulting and program improvement.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.