Relevant bibliographies by topics / Hybrid data mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Hybrid data mining'

Author: Grafiati
Published: 11 September 2023
Last updated: 8 June 2024

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Journal articles on the topic "Hybrid data mining"

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Ambulkar, Bhagyashree, and Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (May 31, 2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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Elankavi, R., R. Kalaiprasath, and R. Udayakumar. "DATA MINING WITH BIG DATA REVOLUTION HYBRID." International Journal on Smart Sensing and Intelligent Systems 10, no. 4 (2017): 560–73. http://dx.doi.org/10.21307/ijssis-2017-270.

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Lakshmi Devasena, C., and M. Hemalatha. "A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm." International Journal of Computer Applications 25, no. 2 (July 31, 2011): 1–5. http://dx.doi.org/10.5120/3007-4056.

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Shadroo, Shabnam, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, Morteza Naserbakht, and Mehdi Hosseinzadeh. "Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems." Acta Informatica Pragensia 10, no. 1 (June 30, 2021): 85–107. http://dx.doi.org/10.18267/j.aip.148.

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Azad, Chandrashekhar. "Data Mining based Hybrid Intrusion Detection System." Indian Journal of Science and Technology 7, no. 6 (June 20, 2014): 781–89. http://dx.doi.org/10.17485/ijst/2014/v7i6.19.

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Sharma, Monica, and Rajdeep Kaur. "Data Mining in Healthcare using Hybrid Approach." International Journal of Computer Applications 128, no. 4 (October 15, 2015): 49–53. http://dx.doi.org/10.5120/ijca2015906539.

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Abidi, Balkis, Sadok Ben Yahia, and Charith Perera. "Hybrid microaggregation for privacy preserving data mining." Journal of Ambient Intelligence and Humanized Computing 11, no. 1 (November 26, 2018): 23–38. http://dx.doi.org/10.1007/s12652-018-1122-7.

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Lee, Zne-Jung, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye, and Zhen Chen. "A hybrid system for imbalanced data mining." Microsystem Technologies 26, no. 9 (August 8, 2019): 3043–47. http://dx.doi.org/10.1007/s00542-019-04566-1.

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Panda, Mrutyunjaya, and Ajith Abraham. "Hybrid evolutionary algorithms for classification data mining." Neural Computing and Applications 26, no. 3 (August 10, 2014): 507–23. http://dx.doi.org/10.1007/s00521-014-1673-2.

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Harrag, Fouzi, and Ali Alshehri. "Applying Data Mining in Surveillance." International Journal of Distributed Systems and Technologies 14, no. 1 (February 10, 2023): 1–24. http://dx.doi.org/10.4018/ijdst.317930.

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In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of these inputs. In this research, a data mining-based framework has been proposed for surveillance. The research includes interpretation of data from different networks using hybrid data mining technique. In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified.
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Dissertations / Theses on the topic "Hybrid data mining"

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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 descriptions as associations between the concept and the preconditions to this concept and employs a relational upgrade of association rule mining search heuristic, APRIORI rule, to effectively prune the search space. The proposed system is a hybrid predictive inductive logic system, which utilizes inverse resolution for generalization of concept instances in the presence of background knowledge and refines these general patterns into frequent and strong concept definitions with a modified APRIORI-based specialization operator. Two versions of the system are tested for three real-world learning problems: learning a linearly recursive relation, predicting carcinogenicity of molecules within Predictive Toxicology Evaluation (PTE) challenge and mesh design. Results of the experiments show that the proposed hybrid method is competitive with state-of-the-art systems.
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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 for addressing both the problem of filtering unreliable markers (framework maps) and the problem of mapping large numbers of markers (comprehensive maps) efficiently. Traditional approaches for eliminating unreliable markers use resampling of the full data set, which has an even higher computational complexity than the original mapping problem. In contrast, the proposed algorithms use a divide-and-conquer strategy to construct framework maps based on clusters that exclude unreliable markers. The clusters of markers are ordered using parallel processing and are then combined to form the complete map. Three algorithms are presented that explore the trade-off between the number of markers included in the framework map and placement accuracy. Since the mapping problem is susceptible to noise, it is often beneficial to remove markers that are not trustworthy. Traditional mapping techniques for building comprehensive maps process all markers together, including unreliable markers, in a single-iteration approach. The accuracy of the constructed maps may be reduced. In this research work, two-stage algorithms are proposed to mapping most markers by first creating a framework map of the reliable markers, and then incrementally adding the remaining markers to construct high quality comprehensive maps. All proposed algorithms have been evaluated on several human chromosomes using radiation hybrid datasets with varying sizes, and also the performance of our proposed algorithms is compared with state-of-the-art RH mapping softwares. Overall, the proposed algorithms are not only much faster than the comparative approaches, but that the quality of the resulting maps is also much higher.
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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 facilitate the extraction of the specifications of 3D visualization technique and will provide a comprehensive knowledge of this technique in a classified manner. After that, all possible combination of 3D visualization technique will review.3D Visualization technique as a popular technique is a concrete foundation for visualization of multi-dimensional datasets, but it has some limitations. To overcome these limitations, previous studies in literature as well as the experiences of professionals will gather. The results will prove the theoretical findings as well as offering new hybrid techniques (combination with 3D visualization and other visual data mining techniques).The contribution of professionals will empower and complement the results of this study, as they can address solutions for the weaknesses of 3D Visualization technique in their business which is new combination of techniques. These combinations of techniques will create the basis for future researches in order to discover new limitations and provide solutions to overcome by use of hybrid techniques.
Program: Magisterutbildning i informatik
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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 interdisciplinary fields, such as computational and systems biology. Machine learning and data mining are disciplines dealing with knowledge discovery from large data, and their application to computational and systems biology has been extremely fruitful. However, the ever-increasing size and complexity of the biological data require novel computational solutions to be developed. This thesis attempts to contribute to these inter-disciplinary fields by deve10ping and applying different ensemble learning methods and hybrid algorithms for solving a variety of problems in computational and systems biology. Through the study of different types of data generated from a variety of biological systems using different high-throughput approaches, we demonstrate that ensemble learning methods and hybrid algorithms are general, flexible, and highly effective tools for computational and systems biology.
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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 bayésien. Les réseaux de neurones bayésiens admettent une distribution de probabilité sur leurs paramètres, qui leur permettent d'estimer différents types d'incertitudes. Tout d'abord, l'incertitude aléatoire qui est liée aux données, mais également l'incertitude épistémique qui quantifie le manque de connaissance que le modèle possède sur la distribution des données. Plus précisément, cette thèse propose un modèle de réseau de neurones bayésien capable d'estimer ces incertitudes dans le cadre d'un problème de régression multivarié. Ce modèle est appliqué dans le contexte du projet ANR "AstroDeep'' à la régression des ellipticités complexes sur des images de galaxies. Ces dernières peuvent être corrompues par différences sources de perturbation et de bruit qui peuvent être estimées de manière fiable par les différentes incertitudes. L'exploitation de ces incertitudes est ensuite étendue à la cartographie de galaxies, puis au "coaching'' du réseau de neurones bayésien. Cette dernière technique consiste à générer des données de plus en plus complexes durant l'apprentissage du modèle afin d'en améliorer les performances. Le problème de l'explicabilité est quant à lui abordé via la recherche d'explications contrefactuelles. Ces explications consistent à identifier quels changements sur les paramètres en entrée auraient conduit à une prédiction différente. Notre contribution dans ce domaine s'appuie sur la génération d'explications contrefactuelles basées sur un autoencodeur variationnel (VAE) et sur un ensemble de prédicteurs entrainés sur l'espace latent généré par le VAE. Cette méthode est plus particulièrement adaptée aux données en haute dimension, telles que les images. Dans ce cas précis, nous parlerons d'explications contrefactuelles visuelles. En exploitant à la fois l'espace latent et l'ensemble de prédicteurs, nous arrivons à produire efficacement des explications contrefactuelles visuelles atteignant un degré de réalisme supérieur à plusieurs méthodes de l'état de l'art
In this thesis, we address the issue of trust in deep learning predictive systems in two complementary research directions. The first line of research focuses on the ability of AI to estimate its level of uncertainty in its decision-making as accurately as possible. The second line, on the other hand, focuses on the explainability of these systems, that is, their ability to convince human users of the soundness of their predictions.The problem of estimating the uncertainties is addressed from the perspective of Bayesian Deep Learning. Bayesian Neural Networks assume a probability distribution over their parameters, which allows them to estimate different types of uncertainties. First, aleatoric uncertainty which is related to the data, but also epistemic uncertainty which quantifies the lack of knowledge the model has on the data distribution. More specifically, this thesis proposes a Bayesian neural network can estimate these uncertainties in the context of a multivariate regression task. This model is applied to the regression of complex ellipticities on galaxy images as part of the ANR project "AstroDeep''. These images can be corrupted by different sources of perturbation and noise which can be reliably estimated by the different uncertainties. The exploitation of these uncertainties is then extended to galaxy mapping and then to "coaching'' the Bayesian neural network. This last technique consists of generating increasingly complex data during the model's training process to improve its performance.On the other hand, the problem of explainability is approached from the perspective of counterfactual explanations. These explanations consist of identifying what changes to the input parameters would have led to a different prediction. Our contribution in this field is based on the generation of counterfactual explanations relying on a variational autoencoder (VAE) and an ensemble of predictors trained on the latent space generated by the VAE. This method is particularly adapted to high-dimensional data, such as images. In this case, they are referred as counterfactual visual explanations. By exploiting both the latent space and the ensemble of classifiers, we can efficiently produce visual counterfactual explanations that reach a higher degree of realism than several state-of-the-art methods
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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 explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. For software testing, a new simulation-based internal variable range coverage metric is proposed with the goal of enhancing the error detection capability of the generated test data when applied as the target metric. For software model checking, we utilize various dynamic analysis methods, such as data mining, swarm intelligence (ant colony optimization), to extract useful high-level information from program execution data. Despite being incomplete, dynamic program execution can still help to uncover important program structure features and variable correlations. The extracted knowledge, such as invariants in different forms, promising control flows, etc., is then used to facilitate code-level program abstraction (under-approximation/over-approximation), and/or state space partition, which in turn improve the performance of property verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. Experimental results demonstrated the effectiveness of our methods in improving the quality as well as performance of software verification. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing â a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods.
Ph. D.
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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 strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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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 intrusion detection system is created, particularly with the support of a multi-class supervised classification technique. In agent-based IDS, there is no central control and therefore no central point of failure. Agents can detect and take predefined actions against malicious activities, which can be detected with the help of data mining techniques. The proposed IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDSs with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on a multiagent platform along with a supervised classification technique. Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. To facilitate information exchange between different agents in the proposed hybrid layered multiagent architecture, a low cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that this system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time on the network.
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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 global solution; however, results of numerical experiments presented in this thesis demonstrate that they, as a rule, calculate a solution which is a global one or close to it. Their applications to data mining problems and the protein folding problem are demonstrated.
Doctor of Philosophy
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Books on the topic "Hybrid data mining"

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Evgenii, Vityaev, ed. Data mining in finance: Advances in relational and hybrid methods. Boston: Kluwer Academic, 2000.

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Bergmeir, Philipp. Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20367-2.

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Daniel, Howard, Ślęzak Dominik, Hong You Sik, and SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Sifeng, Liu, and Lin Yi 1959-, eds. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.

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Lee, Geuk. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Daniel, Howard, Kim Haeng-kon, Kim Tai-hoon, Ko Il-seok, Lee Geuk, Ślęzak Dominik, Sloot Peter 1956-, and SpringerLink (Online service), eds. Advances in Hybrid Information Technology: First International Conference, ICHIT 2006, Jeju Island, Korea, November 9-11, 2006, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.

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Emilio, Corchado, Abraham Ajith 1968-, and Pedrycz Witold 1953-, eds. Hybrid artificial intelligence systems: Third international workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008 : proceedings. Berlin: Springer, 2008.

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Daniel, Howard, Ślęzak Dominik, and SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Lee, Geuk. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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David, Hutchison. Hybrid Artificial Intelligence Systems: 4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Book chapters on the topic "Hybrid data mining"

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Dani, Virendra, Priyanka Kokate, Surbhi Kushwah, and Swapnil Waghela. "Privacy Preserving Data Mining Technique to Secure Distributed Client Data." In Hybrid Intelligent Systems, 565–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_52.

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Du, Mingjing, and Shifei Ding. "L-DP: A Hybrid Density Peaks Clustering Method." In Data Mining and Big Data, 74–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_8.

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Sonawani, Shilpa, and Amrita Mishra. "DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mining." In Advanced Data Mining and Applications, 149–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_14.

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Mucherino, A., and L. Liberti. "A VNS-Based Heuristic for Feature Selection in Data Mining." In Hybrid Metaheuristics, 353–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_13.

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Smith-Miles, Kate, Brendan Wreford, Leo Lopes, and Nur Insani. "Predicting Metaheuristic Performance on Graph Coloring Problems Using Data Mining." In Hybrid Metaheuristics, 417–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_16.

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Li, Kan, Wensi Mu, Yong Luan, and Shaohua An. "A Hybrid-Sorting Semantic Matching Method." In Advanced Data Mining and Applications, 404–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6_36.

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Shafiq, Sobia, Wasi Haider Butt, and Usman Qamar. "Attack Type Prediction Using Hybrid Classifier." In Advanced Data Mining and Applications, 488–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_38.

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Cecotti, Hubert, and Abdel Belaïd. "Hybrid OCR Combination for Ancient Documents." In Pattern Recognition and Data Mining, 646–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_71.

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Lee, Jae Sik, and Jin Chun Lee. "Customer Churn Prediction by Hybrid Model." In Advanced Data Mining and Applications, 959–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_104.

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Rakotomalala, Ricco, Faouzi Mhamdi, and Mourad Elloumi. "Hybrid Feature Ranking for Proteins Classification." In Advanced Data Mining and Applications, 610–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_72.

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Conference papers on the topic "Hybrid data mining"

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Grzymala-Busse, J. W., Z. S. Hippe, T. Mroczek, E. Roj, and B. Skowronski. "Data mining experiments on hop processing data." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.32.

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Tiwari, Anil Kumar, G. Ramakrishna, Lokesh Kumar Sharma, and Sunil Kumar Kashyap. "Neural Network and Genetic Algorithm based Hybrid Data Mining Algorithm (Hybrid Data Mining Algorithm)." In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974485.

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Chung, Sheng-Hao, Wei-Han Chang, and Kawuu W. Lin. "A data mining algorithm for mining region-aware cyclic patterns." In 2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/his.2011.6122195.

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Hambaba, M. L. "Intelligent hybrid system for data mining." In IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr). IEEE, 1996. http://dx.doi.org/10.1109/cifer.1996.501832.

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Suraj, Z., and Delimata. "Data Mining Exploration System for Feature Selection Tasks." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253500.

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Hadzic, F., H. Tan, T. S. Dillon, and E. Chang. "Implications of frequent subtree mining using hybrid support definition." In DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070021.

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Xydas, S., A. S. Hassan, C. E. Marmaras, N. Jenkins, and L. M. Cipcigan. "Electric Vehicle Load Forecasting using Data Mining Methods." In Hybrid and Electric Vehicles Conference 2013 (HEVC 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1914.

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Chen, Chunying, Xiongwei Zhou, and Jianzhong Zhang. "Web Data Mining System Based on Web Services." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.258.

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Putri, Awalia W., and Laksmiwati Hira. "Hybrid transformation in privacy-preserving data mining." In 2016 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2016. http://dx.doi.org/10.1109/icodse.2016.7936114.

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Bellary, Jyothi, Bhargavi Peyakunta, and Sekhar Konetigari. "Hybrid Machine Learning Approach in Data Mining." In 2010 Second International Conference on Machine Learning and Computing. IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.57.

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