Relevant bibliographies by topics / Hybrid data mining / Dissertations / Theses
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Dissertations / Theses on the topic 'Hybrid data mining'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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1

Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.

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Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptio
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Seetan, Raed. "A Data Mining Approach to Radiation Hybrid Mapping." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27315.

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The task of mapping markers from Radiation Hybrid (RH) mapping experiments is typically viewed as equivalent to the traveling-salesman problem, which has combinatorial complexity. As an additional problem, experiments commonly result in some unreliable markers that reduce the overall map quality. Due to the large numbers of markers in current radiation hybrid populations, the use of the data mining techniques becomes increasingly important for reducing both the computational complexity and the impact of noise of the original data. In this dissertation, a clustering-based approach is proposed f
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Zall, Davood. "Visual Data Mining : An Approach to Hybrid 3D Visualization." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16601.

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By increasing the volume and complexity of datasets, Visual Data Mining (VDM), new visualization techniques evolved and new techniques released. However, some of these techniques performing well and cover all expectations; the others failed to save their positions. The main issue of such techniques is problem dependency.In this study, after a short description about necessity of Visual Data Mining techniques, I will provide a classified review of previous researches. This will result in a deep understanding as well as simple accessibility to previous researches, in a concise manner. This will
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Yang, Pengyi. "Ensemble methods and hybrid algorithms for computational and systems biology." Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/28979.

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Modern molecular biology increasingly relies on the application of high-throughput technologies for studying the function, interaction, and integration of genes, proteins, and a variety of other molecules on a large scale. The application of those high throughput technologies has led to the exponential growth of biological data, making modern molecular biology a data-intensive science. Huge effort has been directed to the development of robust and efficient computational algorithms in order to make sense of these extremely large and complex biological data, giving rise to several interdiscipli
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Theobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.

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Dans cette thèse, nous abordons le problème de la confiance que nous pouvons avoir en des systèmes prédictifs de type réseaux profonds selon deux directions de recherche complémentaires. Le premier axe s'intéresse à la capacité d'une IA à estimer de la façon la plus juste possible son degré d'incertitude liée à sa prise de décision. Le second axe quant à lui se concentre sur l'explicabilité de ces systèmes, c'est-à-dire leur capacité à convaincre l'utilisateur humain du bien fondé de ses prédictions. Le problème de l'estimation des incertitudes est traité à l'aide de l'apprentissage profond ba
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Cheng, Xueqi. "Exploring Hybrid Dynamic and Static Techniques for Software Verification." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26216.

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With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that exp
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Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks." Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.

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pande, anurag. "ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3016.

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Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management st
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Sainani, Varsha. "Hybrid Layered Intrusion Detection System." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.

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The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intru
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Zhang, Jiapu. "Derivative-free hybrid methods in global optimization and their applications." Thesis, University of Ballarat, 2005. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34054.

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In recent years large-scale global optimization (GO) problems have drawn considerable attention. These problems have many applications, in particular in data mining and biochemistry. Numerical methods for GO are often very time consuming and could not be applied for high-dimensional non-convex and / or non-smooth optimization problems. The thesis explores reasons why we need to develop and study new algorithms for solving large-scale GO problems .... The thesis presents several derivative-free hybrid methods for large scale GO problems. These methods do not guarantee the calculation of a globa
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Hussain, Mukhtar. "Data-driven discovery of mode switching conditions to create hybrid models of cyber-physical systems." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235043/1/Mukhtar_Hussain_Thesis.pdf.

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Models are essential tools for evaluating a system’s behaviour under different scenarios. However, in industrial practice pre-existing models of cyber-physical systems (CPSs) are not always available because CPSs can be legacy systems which are subject to changes and upgrades over time that may not be well documented. System identification addresses the problem by creating models from the external observation of a system. This research is concerned with hybrid system identification of CPSs, i.e., building models of dynamic systems switching between different operating modes. This thesis presen
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Barak, Sasan. "Technical and Fundamental Features’ analysis for Stock Market Prediction with Data Mining Methods." Doctoral thesis, Università degli studi di Bergamo, 2019. http://hdl.handle.net/10446/128764.

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Of the most important concerns of market practitioners is future information of the companies which offer stocks. A reliable prediction of the company’s financial status provides a situation for the investor to more confident investments and gaining more profits(Huang, 2012b). Accurately prediction of stocks’ prices has a positive affects into the organizations financial stability (Asadi et al., 2012). Since financial market is complex and has non-linear dynamic systems, its prediction is really challenging (Huang and Tsai, 2009). The steady and amazing progress of computer hardware technolo
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Paramasivam, Vijayajothi. "Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/54143.

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This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the present tedious and rigorous CFD methodology being currently adopted to study the influence of geometric parameters on hemodynamics in the human abdominal aortic aneurysm. This approach is seen as a “marriage” between medicine and computer domains.
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Cheng, Iunniang. "Hybrid Methods for Feature Selection." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1244.

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Feature selection is one of the important data preprocessing steps in data mining. The feature selection problem involves finding a feature subset such that a classification model built only with this subset would have better predictive accuracy than model built with a complete set of features. In this study, we propose two hybrid methods for feature selection. The best features are selected through either the hybrid methods or existing feature selection methods. Next, the reduced dataset is used to build classification models using five classifiers. The classification accuracy was evaluated i
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Lin, Pengpeng. "A Framework for Consistency Based Feature Selection." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/62.

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Feature selection is an effective technique in reducing the dimensionality of features in many applications where datasets involve hundreds or thousands of features. The objective of feature selection is to find an optimal subset of relevant features such that the feature size is reduced and understandability of a learning process is improved without significantly decreasing the overall accuracy and applicability. This thesis focuses on the consistency measure where a feature subset is consistent if there exists a set of instances of length more than two with the same feature values and the sa
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Alsalama, Ahmed. "A Hybrid Recommendation System Based on Association Rules." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1250.

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Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small
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Pagnossim, José Luiz Maturana. "Uma abordagem híbrida para sistemas de recomendação de notícias." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-07062018-101232/.

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Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e efic
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Alencar, Medeiros Gabriel Henrique. "ΡreDiViD Τοwards the Ρredictiοn οf the Disseminatiοn οf Viral Disease cοntagiοn in a pandemic setting". Electronic Thesis or Diss., Normandie, 2025. http://www.theses.fr/2025NORMR005.

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Les systèmes de surveillance basés sur les événements (EBS) sont essentiels pour détecter et suivre les phénomènes de santé émergents tels que les épidémies et crises sanitaires. Cependant, ils souffrent de limitations, notamment une forte dépendance à l’expertise humaine, des difficultés à traiter des données textuelles hétérogènes et une prise en compte insuffisante des dynamiques spatio-temporelles. Pour pallier ces limites, nous proposons une approche hybride combinant des méthodologies guidées par les connaissances et les données, ancrée dans l’ontologie des phénomènes de propagation (Pro
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Jiang, Xinxin. "Mining heterogeneous enterprise data." Thesis, 2018. http://hdl.handle.net/10453/129377.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>Heterogeneity is becoming one of the key characteristics inside enterprise data, because the current nature of globalization and competition stress the importance of leveraging huge amounts of enterprise accumulated data, according to various organizational processes, resources and standards. Effectively deriving meaningful insights from complex large-scaled heterogeneous enterprise data poses an interesting, but critical challenge. The aim of this thesis is to investigate the theoretical foundations of mini
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Babu, T. Ravindra. "Large Data Clustering And Classification Schemes For Data Mining." Thesis, 2006. https://etd.iisc.ac.in/handle/2005/440.

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Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are large. Largeness of data is characterized by its size which is beyond the capacity of main memory of a computer. Data Mining is an interdisciplinary field involving database systems, statistics, machine learning, visualization and computational aspects. The focus of data mining algorithms is scalability and efficiency. Large data clustering and classification is an important
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Babu, T. Ravindra. "Large Data Clustering And Classification Schemes For Data Mining." Thesis, 2006. http://hdl.handle.net/2005/440.

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Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are large. Largeness of data is characterized by its size which is beyond the capacity of main memory of a computer. Data Mining is an interdisciplinary field involving database systems, statistics, machine learning, visualization and computational aspects. The focus of data mining algorithms is scalability and efficiency. Large data clustering and classification is an important
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蔡明憲. "A Hybrid Data Mining Model for Customer Retention." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/25689304585306477235.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>90<br>Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another. This thesis proposes a hybrid architecture that tackles the complete customer retention problem, in the sense that it not only predicts churn probability but also proposes retention policies. The architecture works in two modes, namely, the learning and usage modes. In the learning mode, the churn model learner learns potential associations inside the historic
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Tzu-Fan, Tang, and 湯子範. "A hybrid data mining approach for customer relationship management." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/20933792886165601712.

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碩士<br>國立中正大學<br>會計與資訊科技研究所<br>96<br>It is the fact that current domestic and foreign enterprises have been facing an unprecedented competition. The ‘product-oriented’ model has been transferred to the ‘customer-oriented’ one. This results in the importance of Customer relationship management (CRM). Customer retention is one major problem in CRM. Data mining techniques have been applied to predict the loss of customers (or customer churn). In literature, they have been proven its applicability in customer churn prediction. In this thesis, hybrid data mining methods are developed in order to imp
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Yang, Ren-fu, and 楊仁富. "Hybrid Data Mining and MSVM for Short Term Load Forecasting." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28661440234858506249.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>98<br>The accuracy of load forecast has a significant impact for power companies on executing the plan of power development, reducing operating costs and providing reliable power to the client. Short-term load forecasting is to forecast load demand for the duration of one hour or less. This study presents a new approach to process load forecasting. A Support Vector Machine (SVM) was used for the initial load estimation. Particle Swarm Optimization (PSO) was then adopted to search for optimal parameters for the SVM. In doing the load forecast, training data is the m
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Chen, Lei Chun, and 陳蕾淳. "A Hybrid Data Mining Model in Analyzing Corporate Social Responsibility." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/07858790424620614969.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>101<br>Over the past two decades, Corporate Social Responsibility (CSR) has received worldwide attention. Publication of CSR Reports has become the trend for domestic and foreign enterprises. In the constantly changing competition environment, it will be focus of public attention that how enterprises to play the role of corporate citizenship and to achieve a balance in profit, environmental and charitable activities. However, most of previous quantitative studies of CSR concentrate on traditional statistic approaches. The data mining technique has not been widely ex
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Lee, Chia-Hsun, and 李嘉訓. "A Hybrid Data Mining Approach to Quality Control of Machining Process." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/31833725094107767456.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>94<br>Nowadays quality is one of the best sources of competitive advantage. High quality performance is becoming of critical importance. Quality control is a process employed to ensure a certain level of quality in a product or service. One of the techniques in quality control is to predict the product quality abased on the product features. However, traditional quality control techniques have some weaknesses such as specific control limits, heavily on the collection and analysis of data and uncertainty processing. In order to promote the effectiveness of quality co
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Lu, Chi-Jie, and 呂奇傑. "Hybrid Neural Network Classification Techniques in the Application of Data Mining." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/37742016816981561250.

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碩士<br>輔仁大學<br>應用統計學研究所<br>89<br>Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most case, the estimation of parameters or the classification results can not really describe the realization of business modeling. The artificial neural network is becoming a very popular alternative in prediction and classification task due to its associat
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Chen, Hsiao-ming, and 陳小明. "Prevention of Drug Dispensing Errors by Using Hybrid Data Mining Approaches." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/61860258367373868836.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>96<br>One important issue in medical care is the prevention of drug dispensing errors since they caused numerous injuries and deaths with expensive cost. In this thesis, we propose a hybrid data mining approach with an implemented system to solve this problem. Our approach consists of two main modules, HDMmodel and HDMclustering. In HDMmodel, J48 and logistic regression are used to derive the decision tree and regression function from the given dispensing error cases and drug database. In HDMclustering, similar drugs, which are easily confused with each other, are
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Fan, Ching-Yi, and 范景怡. "Applying Data Mining Techniques to Combine Predictions in Hybrid Recommender Systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/92064178136185259961.

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碩士<br>中國文化大學<br>資訊管理學系碩士在職專班<br>101<br>Nowadays, the Recommender System has been developed in several different ways for operating. The main techniques are used to develop Recommender System: CB (Content-Based), CF (Collaborative Filtering) and DF (Demographic Filtering). However, each technique has its advantages and limitations. For this reason, many scholars have proposed combine several techniques, intended to reduce the disadvantages of a single method, and achieve more precise recommendation. Currently, the main techniques are used to develop Recommender System, mostly according to the
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Shu, I.-Ping, and 徐一平. "Study of Hybrid Data Mining Techniques Applied for Filtering Spam Mail." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/00992363360301001047.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>97<br>The network has been established and developed since 1970; people have generally used the network. People artificially delivered mail before, but this tendency was transferred to E-mail. The time and distance of communication were decreased by E-mail, and E-mail gradually changed our live and working way. At this moment, some beneficial people use the malicious programs or collect the email boxes in many ways, then send email arbitrarily. It has been perplexed to the receiver. This study (GA/DT) adopts the genetic algorithm (Genetic Algorithms, GA) and decision
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呂奇傑. "Hybrid Neural Network Classification Techniques in the Application of Data Mining." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/76303042740984553449.

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碩士<br>輔仁大學<br>應用統計研究所<br>89<br>Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most case, the estimation of parameters or the classification results can not really describe the realization or business modeling. The artificial neural network is becoming a very popular alternative in prediction and classification task due to its associate
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Kumar, Nishant. "Sentiment Analysis Using Hybrid Machine Learning Technique." Thesis, 2016. http://ethesis.nitrkl.ac.in/8616/1/2016_MT_214CS3513_Nishant_Kumar.pdf.

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It is observed that consumers often share their opinion, views or feeling about any term used on social network in the form of reviews, comments or feedback. Those feedbacks given by end users have a great impact for evolution of new version of any product. Due to this trend in social media in recent years, sentiment analysis has become an important concern for theoreticians and practitioners Moreover reviews are often written in natural language and are mostly unstructured. Thus, to obtain any meaningful information from these reviews, it needs to be processed. Due to large size of data it is
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Kang, Shu-Tyng, and 康舒婷. "Applying Hybrid Data Mining Approach to Develop a Cerebrovascular Disease Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/70886576446134037009.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>101<br>With Taiwan’s economic take-off, Taiwanese people gradually placed importance on the health and medical issues. According to the data reported by WHO, stroke has become a big threat of health in the developed countries since 1999. In Taiwan, stroke is the third of the top ten causes of deaths. Therefore, how to prevent and discover stroke is very important issue now. The best way to examine and diagnose stroke is using the brain image examination and the carotid ultrasound. However, the price of these examinations is excessively higher than others. If people d
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Herani, Inggi Rengganing, and Inggi Rengganing Herani. "Development of Carotid Artery Diagnostic Prediction Model using Hybrid Data Mining Approach." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/67320389316269101304.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>101<br>Carotid artery disease is the main caused of disability and death related with stroke or cerebrovascular disease, and in the worldwide medical issue, stroke was responsible for the high number of death. Because there are no symptoms of carotid artery disease, it is important to perform medical test using ultrasound or imaging method to visualize the carotid arteries. This kind of test is uncomfortable, expensive, and has some risks. Therefore, to reduce the risks and economic issue, this research presents method that generates some important information for th
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Feng, Hsin-lan, and 馮欣嵐. "Applying a Hybrid Data Mining Approach to Develop a Stroke Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/75650209307133005293.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>101<br>Stroke has become a big threat of health for people worldwide, the death rate and disable rate of stroke are both high. Therefore, how to prevent stroke and discover it is an important issue now. The best way to examine and discover stroke is the brain image examination and ultrasound, however, the price of these examinations is relatively high. People won’t take these examinations if there is no advice from doctor or no obvious symptom people feel. Consequently, we want to use normal healthy examination that is cheaper and easy to take to be the basic of our
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Wang, Yu-Chung, and 王鈺中. "Evaluating Renewable Energy Policies Using Hybrid Data Mining and Analytic Hierarchy Process Modeling." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/68531396159632779020.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>102<br>When a large percentage of energy (>90%) is generated by fossil fuel, carbon dioxide emissions increase the greenhouse effect. Therefore, renewable, sustainable, and economically viable energy sources are needed as alternatives to fossil fuels. The facilities and installation costs for generating renewable energy is much higher than the cost of fossil fuel facilities. Thus, governments need effective policies, regulations, and incentive programs to promote the usage of renewable energy. Renewable energy can be classified into different categories, includin
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Tseng, Jui-Chih, and 曾瑞智. "A Hybrid Data Mining Approach to Construct the Target Customers Choice Reference Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/26022623832684281818.

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碩士<br>大同大學<br>資訊經營學系(所)<br>101<br>Marketing, the prevailing commercial activity of enterprises, is an important strategy to increase customer loyalty and potential customer for more profit. To maximize profit with limited resources, it would be more profitable for enterprises to choose the right target customers. Therefore, it is necessary to build up an efficient, objective and accurate target customer choice model. Using data mining techniques to find the target customers is a traditional way. However, researches in the past mainly focused on finding the high accuracy classifier, but differe
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蔡永順. "An RFID-based Data Mining Using Hybrid and Heuristic Methods for Quality Management." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/82687441413491623671.

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博士<br>國立交通大學<br>資訊管理研究所<br>101<br>Many enterprises are confronting global competition and shortened life cycle of new products now. Therefore, if they can not master product quality, they will delay the product development as well as time to market and can not even provide product variety immediately. The data mining can find hidden knowledge patterns in data and enable complex business processes to be understood and reengineered. In addition, RFID can effortlessly turn every object into mobile network nodes which can be tracked, traced, monitored, trigger actions, or respond to action request
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Yu, Ting-Yi, and 尤婷藝. "Using A Hybrid Meta-evolutionary Algorithm for Mining Classification Rules Through Microarray Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/5ns46j.

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碩士<br>國立虎尾科技大學<br>資訊管理研究所<br>98<br>With the rapid development of information technology, microarray data is an important field of study for cancer research. However, microarray data is with high dimensional attributes and small sample size resulting in lengthy computation time and low classification accuracy. Due to gene microarray data classification issues, how to get more accurate prediction results with better quality becomes an important area of research. This thesis has proposed a hybrid evolutionary algorithm which combines a genetic algorithm and binary particle swarm optimization with
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(7054517), Syed Zahid Hassan. "A novel hybrid data mining approach for knowledge extraction and classification in medical databases." Thesis, 2008. https://figshare.com/articles/thesis/A_novel_hybrid_data_mining_approach_for_knowledge_extraction_and_classification_in_medical_databases/21443082.

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<p>Over the past several years, there has been an explosion in the amount of medical data generated and subsequently collected in medical domain. Data mining techniques have been used extensively in mining the medical data. Obtaining high quality data mining results is very challenging because of the inconsistency of the results of different data mining algorithms and noise in the medical data.</p> <p>This thesis presents a novel hybrid data mining approach for knowledge extraction and classification in medical databases. The proposed approach is formulated to cluster extracted features from m
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Guo, Mu-Liang, and 郭木良. "A Hybrid System Integrating Data Mining and Artificial Intelligence Approaches for Stock Price Prediction." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/30048291361050573695.

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碩士<br>國立中正大學<br>財務金融研究所<br>102<br>In this study, we develop a new hybrid stock prediction system by integrating data mining and artificial intelligence techniques. Different from other studies, this study proposes a system that does not predict stock price using these techniques directly. We posit that technical indicators are not always effective. Each indicator is affected by other indicators and fundamentalist factors. Consequently, the proposed system integrates these two techniques to optimize their advantages based on technical and fundamental indicators. We conduct two experiments to ex
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HUANG, TING-XUAN, and 黃婷萱. "A Hybrid Data Mining Model for Analyzing the Association between Diabetes and Breast Cancer." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/59205764816125214266.

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碩士<br>輔仁大學<br>企業管理學系管理學碩士班<br>104<br>Diabetes is a chronic disease which cannot be cured by medical technology nowadays, it death population created by complications of diabetes increasing year by year, and breast cancer brings huge medical expenses, and it becomes the burden of the National Health Insurance. The relevance between diabetes and cancer is a well-known issue in recent years, among all the cancer, the incidence of breast cancer is the highest in Taiwanese female. Therefore, the purpose of this study is applying data mining techniques to retrospective cohort study the association
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Chen, Chien-Wei, and 陳建維. "Development of Real Time Production Control System in FAB By Hybrid Data Mining Approach." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/60626635834370788788.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>96<br>Using machine learning-based real time dispatching rule selection mechanism to develop knowledge bases (KBs) for production control system (PCS) has shown encouraging results in recent research. However, there is still little research focusing on employed real time dispatching rule selection mechanism to improve production performance in semiconductor wafer fabri-cation factories PCS. Moreover, due to short product life cycles, most actual FABs produce multiple products and the product mix changes from time to time. All of earlier work of machine learning-based
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Li, Jie-Ruei, and 李睿傑. "An Intelligent Vehicular Maintenance and Replacement System in Distribution Services: A Hybrid Data Mining Technique." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/16632688345502903558.

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碩士<br>輔仁大學<br>資訊管理學系<br>97<br>As e-commerce has grown exponentially, the business of the distribution service is also growing up and expanding quickly for recent years. Namely, e-commerce not only changes customers’ shopping behaviors to bring new opportunities to the B2C marketspace. Nevertheless, from the perspective of merchant-side, the maintenance, repair and operations (MRO) fees of vehicles in distribution service is also increasingly dramatically. In this project, we develop an intelligent maintenance and replacement system to help the manager and technicians conduct preventive mainten
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Chauhan, Ajay Singh. "Financial statement fraud detection Model based on Hybrid data mining methods: Proposing an optimized Detection model." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17200.

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Rodic, Daniel. "A Hybrid heuristic-exhaustive search approach for rule extraction." Diss., 2001. http://hdl.handle.net/2263/25095.

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The topic of this thesis is knowledge discovery and artificial intelligence based knowledge discovery algorithms. The knowledge discovery process and associated problems are discussed, followed by an overview of three classes of artificial intelligence based knowledge discovery algorithms. Typical representatives of each of these classes are presented and discussed in greater detail. Then a new knowledge discovery algorithm, called Hybrid Classifier System (HCS), is presented. The guiding concept behind the new algorithm was simplicity. The new knowledge discovery algorithm is loosely based on
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Liu, Minhui. "Multivariate nonnormal regression models, information complexity, and genetic algorithms a three way hybrid for intelligent data mining /." 2006. http://etd.utk.edu/2006/LiuMinhui.pdf.

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Yang, Chun-Yi, and 楊竣壹. "A Hybrid of Data Mining and Statistical Analysis Approach on Association between Pulmonary Tuberculosis and Lung Cancer." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/45849810654231649880.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>102<br>Background and objective: Being as a global infectious disease and top 10 most fatal cancers in Taiwan, it is important to acquire the clinical pathology of tuberculosis (TB) and lung cancer. This study explored the association of tuberculosis and lung cancer with other comorbidities and investigated whether any featured attribute could be critical factor in influence of the risk of lung cancer among TB patients by conducting a hybrid data mining and statistical approach. Methods: Study objects were be identified from the NHIRD with diagnosis of tuberculosis b
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(9028061), Chenxi Xiong. "HYBRID FEATURE SELECTION IN NETWORK INTRUSION DETECTION USING DECISION TREE." Thesis, 2020.

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The intrusion detection system has been widely studied and deployed by researchers for providing better security to computer networks. The increasing of the attack volume and the dramatic advancement of the machine learning make the cooperation between the intrusion detection system and machine learning a hot topic and a promising solution for the cybersecurity. Machine learning usually involves the training process using huge amount of sample data. Since the huge input data may cause a negative effect on the training and detection performance of the machine learning model. Feature selection b
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Wang, Shu-Chao, and 王淑昭. "The Factors Affecting Academic Achievement for the 5th and 6th Grade Elementary School Students by Hybrid Data Mining Approach." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75460218666045377079.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>96<br>A total number of 485 5th and 6th grade students of effective samples were all from an elementary school in Taipei country during 2004 to 2006. To resolve student academic achievement problem, this study develops a hybrid Genetic Algorithm/Decision Tree (i.e., GA/DT) approach. Then, the proposed GA/DT approach compares with DT, factors analysis combining DT, and correlation combining DT. The study results indicated that the key attributes of 5th and 6th grade elementary school students academic achievement include mother’s age, father’s academic history, parent
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