Relevant bibliographies by topics / Data Mining Approaches

Academic literature on the topic 'Data Mining Approaches'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data Mining Approaches"

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Mamitsuka, Hiroshi. "Glycoinformatics: Data Mining-based Approaches." CHIMIA International Journal for Chemistry 65, no. 1 (February 23, 2011): 10–13. http://dx.doi.org/10.2533/chimia.2011.10.

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Xiao, Wenke, Lijia Jing, Yaxin Xu, Shichao Zheng, Yanxiong Gan, and Chuanbiao Wen. "Different Data Mining Approaches Based Medical Text Data." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/1285167.

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The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.
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Meng, Jun, Xiao Chen, Tian Yu Zhu, and Yang Yang Pan. "Data Mining Approaches in Manpower Evaluation." Applied Mechanics and Materials 513-517 (February 2014): 750–53. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.750.

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Manpower allocation determines the competence of the companies. Scientific manpower allocation calls for accurate evaluation on the abilities that the employees have for the posts. In this paper, we first present a general fuzzy clustering model for manpower evaluation for companies. To verify the approach, a new distance-based evaluation model is also presented. Simulation results demonstrated the accuracy of our research.
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Cordero, F., M. Botta, and R. A. Calogero. "Microarray data analysis and mining approaches." Briefings in Functional Genomics and Proteomics 6, no. 4 (January 22, 2008): 265–81. http://dx.doi.org/10.1093/bfgp/elm034.

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Böcker, Alexander, Gisbert Schneider, and Andreas Teckentrup. "Status of HTS Data Mining Approaches." QSAR & Combinatorial Science 23, no. 4 (June 2004): 207–13. http://dx.doi.org/10.1002/qsar.200330860.

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Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 14, no. 3-4 (February 2009): 147–54. http://dx.doi.org/10.1016/j.drudis.2008.12.005.

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Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 17 (February 2012): S16—S23. http://dx.doi.org/10.1016/j.drudis.2011.12.006.

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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (January 2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given
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SRIRAAM, N., V. NATASHA, and H. KAUR. "DATA MINING APPROACHES FOR KIDNEY DIALYSIS TREATMENT." Journal of Mechanics in Medicine and Biology 06, no. 02 (June 2006): 109–21. http://dx.doi.org/10.1142/s0219519406001893.

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Data mining techniques has been used as a recent trend for achieving diagnostics results, especially in medical fields such as kidney dialysis, skin cancer and breast cancer detection, and also biological sequences classification. Due to its ability to discover the relationship and pattern of the medical database, early detection or prediction of pathological conditions through mining has become feasible. This paper discusses the data mining approach for parametric evaluation to improve the treatment of kidney dialysis patient. The experimental result shows that classification accuracy using Association mining between the ranges 50–97.7% is obtained based on the dialysis parameter combination. Such a decision-based approach helps the clinician to decide the level of dialysis required for individual patient.
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Fridman, Olga V. "Data Mining - methods and algorithms, summary." Transaction Kola Science Centre 12, no. 5-2021 (December 27, 2021): 91–103. http://dx.doi.org/10.37614/2307-5252.2021.5.12.008.

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The article provides a brief overview of Data Mining methods and algorithms which are used in solving various tasks where both quantitative and qualitative data have to be processed. The purpose of the review is a brief description of the methods and algorithms, as well as a list of sources in which they are described in detail. The features of existing approaches to solving such problems are considered, the analysis of modern methods for solving Data Mining problems is carried out.
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Dissertations / Theses on the topic "Data Mining Approaches"

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Liu, Xiao, and Xiao Liu. "Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/620913.

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Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug events (WHO 2004). Post-approval adverse drug events are a major health concern. They attribute to about 700,000 emergency department visits, 120,000 hospitalizations, and $75 billion in medical costs annually (Yang et al. 2014). However, certain adverse drug events are preventable if detected early. Timely and accurate pharmacovigilance in the post-approval period is an urgent goal of the public health system. The availability of various sources of healthcare data for analysis in recent years opens new opportunities for the data-driven pharmacovigilance research. In an attempt to leverage the emerging healthcare big data, pharmacovigilance research is facing a few challenges. Most studies in pharmacovigilance focus on structured and coded data, and therefore miss important textual data from patient social media and clinical documents in EHR. Most prior studies develop drug safety surveillance systems using a single data source with only one data mining algorithm. The performance of such systems is hampered by the bias in data and the pitfalls of the data mining algorithms adopted. In my dissertation, I address two broad research questions: 1) How do we extract rich adverse drug event related information in textual data for active drug safety surveillance? 2) How do we design an integrated pharmacovigilance system to improve the decision-making process for drug safety regulatory intervention? To these ends, the dissertation comprises three essays. The first essay examines how to develop a high-performance information extraction framework for patient reports of adverse drug events in health social media. I found that medical entity extraction, drug-event relation extraction, and report source classification are necessary components for this task. In the second essay, I address the scalability issue of using social media for pharmacovigilance by proposing a distant supervision approach for information extraction. In the last essay, I develop a MetaAlert framework for pharmacovigilance with advanced text mining and data mining techniques to provide timely and accurate detection of adverse drug reactions. Models, frameworks, and design principles proposed in these essays advance not only pharmacovigilance research, but also more broadly contribute to health IT, business analytics, and design science research.
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Ma, Yao. "Financial market predictions using Web mining approaches /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20MAY.

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Otaki, Keisuke. "Algorithmic Approaches to Pattern Mining from Structured Data." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215673.

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The contents of Chapter 6 are based on work published in IPSJ Transactions on Mathematical Modeling and Its Applications, vol.9(1), pp.32-42, 2016.
Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第19846号
情博第597号
新制||情||104(附属図書館)
32882
京都大学大学院情報学研究科知能情報学専攻
(主査)教授 山本 章博, 教授 鹿島 久嗣, 教授 阿久津 達也
学位規則第4条第1項該当
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Yang, L. "Optimisation approaches for data mining in biological systems." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1473809/.

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The advances in data acquisition technologies have generated massive amounts of data that present considerable challenge for analysis. How to efficiently and automatically mine through the data and extract the maximum value by identifying the hidden patterns is an active research area, called data mining. This thesis tackles several problems in data mining, including data classification, regression analysis and community detection in complex networks, with considerable applications in various biological systems. First, the problem of data classification is investigated. An existing classifier has been adopted from literature and two novel solution procedures have been proposed, which are shown to improve the predictive accuracy of the original method and significantly reduce the computational time. Disease classification using high throughput genomic data is also addressed. To tackle the problem of analysing large number of genes against small number of samples, a new approach of incorporating extra biological knowledge and constructing higher level composite features for classification has been proposed. A novel model has been introduced to optimise the construction of composite features. Subsequently, regression analysis is considered where two piece-wise linear regression methods have been presented. The first method partitions one feature into multiple complementary intervals and ts each with a distinct linear function. The other method is a more generalised variant of the previous one and performs recursive binary partitioning that permits partitioning of multiple features. Lastly, community detection in complex networks is investigated where a new optimisation framework is introduced to identify the modular structure hidden in directed networks via optimisation of modularity. A non-linear model is firstly proposed before its linearised variant is presented. The optimisation framework consists of two major steps, including solving the non-linear model to identify a coarse initial partition and a second step of solving repeatedly the linearised models to re fine the network partition.
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Yun, Unil. "New approaches to weighted frequent pattern mining." Texas A&M University, 2005. http://hdl.handle.net/1969.1/5003.

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Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mining datasets to obtain the frequent patterns, there is no way to adjust the number of frequent patterns through user feedback, except for changing the minimum support. Alternative measures for mining frequent patterns have been suggested to address these issues. One of the main limitations of the traditional approach for mining frequent patterns is that all items are treated uniformly when, in reality, items have different importance. For this reason, weighted frequent pattern mining algorithms have been suggested that give different weights to items according to their significance. The main focus in weighted frequent pattern mining concerns satisfying the downward closure property. In this research, frequent pattern mining approaches with weight constraints are suggested. Our main approach is to push weight constraints into the pattern growth algorithm while maintaining the downward closure property. We develop WFIM (Weighted Frequent Itemset Mining with a weight range and a minimum weight), WLPMiner (Weighted frequent Pattern Mining with length decreasing constraints), WIP (Weighted Interesting Pattern mining with a strong weight and/or support affinity), WSpan (Weighted Sequential pattern mining with a weight range and a minimum weight) and WIS (Weighted Interesting Sequential pattern mining with a similar level of support and/or weight affinity) The extensive performance analysis shows that suggested approaches are efficient and scalable in weighted frequent pattern mining.
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Shao, Huijuan. "Temporal Mining Approaches for Smart Buildings Research." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/84349.

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With the advent of modern sensor technologies, significant opportunities have opened up to help conserve energy in residential and commercial buildings. Moreover, the rapid urbanization we are witnessing requires optimized energy distribution. This dissertation focuses on two sub-problems in improving energy conservation; energy disaggregation and occupancy prediction. Energy disaggregation attempts to separate the energy usage of each circuit or each electric device in a building using only aggregate electricity usage information from the meter for the whole house. The second problem of occupancy prediction can be accomplished using non-invasive indoor activity tracking to predict the locations of people inside a building. We cast both problems as temporal mining problems. We exploit motif mining with constraints to distinguish devices with multiple states, which helps tackle the energy disaggregation problem. Our results reveal that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices. For the second problem we propose time-gap constrained episode mining to detect activity patterns followed by the use of a mixture of episode generating HMM (EGH) models to predict home occupancy. Finally, we demonstrate that the mixture EGH model can also help predict the location of a person to address non-invasive indoor activities tracking.
Ph. D.
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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.

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Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our second approach dynamically evaluates association rules according to a composite and collective effect of multiple measures. It interactively snapshots the end user’s domain- dependent requirements in evaluating association rules. In essence, our approach uses neural networks along with back-propagation learning to capture the relative importance of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches.
viii, 85 leaves : ill. ; 29 cm
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Shen, Shijun. "Approaches to creating anonymous patient database." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1693.

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Thesis (M.S.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains v, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-68).
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Mougel, Pierre-Nicolas. "Finding homogeneous collections of dense subgraphs using constraint-based data mining approaches." Thesis, Lyon, INSA, 2012. http://www.theses.fr/2012ISAL0073.

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Ce travail de thèse concerne la fouille de données sur des graphes attribués. Il s'agit de graphes dans lesquels des propriétés, encodées sous forme d'attributs, sont associées à chaque sommet. Notre objectif est la découverte, dans ce type de données, de sous-graphes organisés en plusieurs groupes de sommets fortement connectés et homogènes au regard des attributs. Plus précisément, nous définissons l'extraction sous contraintes d'ensembles de sous-graphes densément connectés et tels que les sommets partagent suffisamment d'attributs. Pour cela nous proposons deux familles de motifs originales ainsi que les algorithmes justes et complets permettant leur extraction efficace sous contraintes. La première famille, nommée Ensembles Maximaux de Cliques Homogènes, correspond à des motifs satisfaisant des contraintes concernant le nombre de sous-graphes denses, la taille de ces sous-graphes et le nombre d'attributs partagés. La seconde famille, nommée Collections Homogènes de k-cliques Percolées emploie quant à elle une notion de densité plus relaxée permettant d'adapter la méthode aux données avec des valeurs manquantes. Ces deux méthodes sont appliquées à l'analyse de deux types de réseaux, les réseaux de coopérations entre chercheurs et les réseaux d'interactions de protéines. Les motifs obtenus mettent en évidence des structures utiles dans un processus de prise de décision. Ainsi, dans un réseau de coopérations entre chercheurs, l'analyse de ces structures peut aider à la mise en place de collaborations scientifiques entre des groupes travaillant sur un même domaine. Dans le contexte d'un graphe de protéines, les structures exhibées permettent d'étudier les relations entre des modules de protéines intervenant dans des situations biologiques similaires. L'étude des performances en fonction de différentes caractéristiques de graphes attribués réels et synthétiques montre que les approches proposées sont utilisables sur de grands jeux de données
The work presented in this thesis deals with data mining approaches for the analysis of attributed graphs. An attributed graph is a graph where properties, encoded by means of attributes, are associated to each vertex. In such data, our objective is the discovery of subgraphs formed by several dense groups of vertices that are homogeneous with respect to the attributes. More precisely, we define the constraint-based extraction of collections of subgraphs densely connected and such that the vertices share enough attributes. To this aim, we propose two new classes of patterns along with sound and complete algorithms to compute them efficiently using constraint-based approaches. The first family of patterns, named Maximal Homogeneous Clique Set (MHCS), contains patterns satisfying constraints on the number of dense subgraphs, on the size of these subgraphs, and on the number of shared attributes. The second class of patterns, named Collection of Homogeneous k-clique Percolated components (CoHoP), is based on a relaxed notion of density in order to handle missing values. Both approaches are used for the analysis of scientific collaboration networks and protein-protein interaction networks. The extracted patterns exhibit structures useful in a decision support process. Indeed, in a scientific collaboration network, the analysis of such structures might give hints to propose new collaborations between researchers working on the same subjects. In a protein-protein interaction network, the analysis of the extracted patterns can be used to study the relationships between modules of proteins involved in similar biological situations. The analysis of the performances, on real and synthetic data, with respect to different attributed graph characteristics, shows that the proposed approaches scale well for large datasets
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Johansson, Fernstad Sara. "Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data." Doctoral thesis, Linköpings universitet, Medie- och Informationsteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70860.

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Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex. This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables. The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis. Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.
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Books on the topic "Data Mining Approaches"

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1937-, Lin Tsau Y., ed. Foundations and novel approaches in data mining. Berlin: Springer, 2006.

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Young Lin, Tsau, Setsuo Ohsuga, Churn-Jung Liau, and Xiaohua Hu, eds. Foundations and Novel Approaches in Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11539827.

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Biodata mining and visualization: Novel approaches. Singapore: World Scientific, 2010.

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Taniar, David, and Li Chen. Integrations of data warehousing, data mining and database technologies: Innovative approaches. Hershey, PA: Information Science Reference, 2011.

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library, Wiley online, ed. Pharmaceutical data mining: Approaches and applications for drug discovery. Hoboken, N.J: Wiley, 2010.

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Pharmaceutical data mining: Approaches and applications for drug discovery. Hoboken, N.J: Wiley, 2010.

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Lakshmanan, Valliappa, Eric Gilleland, Amy McGovern, and Martin Tingley, eds. Machine Learning and Data Mining Approaches to Climate Science. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17220-0.

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1965-, Sanger James, ed. The text mining handbook: Advanced approaches in analyzing unstructured data. New York: Cambridge University Press, 2006.

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Feldman, Ronen. The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press, 2007.

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Shawkat Ali, A. B. M. and Xiang Yang 1975-, eds. Dynamic and advanced data mining for progressing technological development: Innovations and systemic approaches. Hershey, PA: Information Science Reference, 2010.

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Book chapters on the topic "Data Mining Approaches"

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Raghavan, Vijay, and Alaaeldin Hafez. "Dynamic Data Mining." In Intelligent Problem Solving. Methodologies and Approaches, 220–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45049-1_27.

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Mucherino, Antonio, Petraq J. Papajorgji, and Panos M. Pardalos. "Statistical Based Approaches." In Data Mining in Agriculture, 23–45. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88615-2_2.

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Kramer, Stefan, Nada Lavrač, and Peter Flach. "Propositionalization Approaches to Relational Data Mining." In Relational Data Mining, 262–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_11.

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Kirsten, Mathias, Stefan Wrobel, and Tamás Horváth. "Distance Based Approaches to Relational Learning and Clustering." In Relational Data Mining, 213–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_9.

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Baldini, Paolo, Silvia Figini, and Paolo Giudici. "Nonparametric Approaches for e-Learning Data." In Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, 548–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790853_43.

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Bessiere, Christian, Abderrazak Daoudi, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Younes Mechqrane, Nina Narodytska, Claude-Guy Quimper, and Toby Walsh. "New Approaches to Constraint Acquisition." In Data Mining and Constraint Programming, 51–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50137-6_3.

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Halkidi, Maria, and Michalis Vazirgiannis. "Quality Assessment Approaches in Data Mining." In Data Mining and Knowledge Discovery Handbook, 613–39. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_31.

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Simovici, Dan. "Intelligent Data Analysis Techniques—Machine Learning and Data Mining." In Artificial Intelligent Approaches in Petroleum Geosciences, 1–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16531-8_1.

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Freitas, Alex A., and Simon H. Lavington. "Approaches to Speed Up Data Mining." In Mining Very Large Databases with Parallel Processing, 89–108. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_10.

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Nakayama, Hirotaka. "MOP/GP Approaches to Data Mining." In Multi-Objective Programming and Goal Programming, 27–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36510-5_3.

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Conference papers on the topic "Data Mining Approaches"

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Grzymala-Busse, J. W. "A comparison of traditional and rough set approaches to missing attribute values in data mining." In DATA MINING 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/data090161.

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Meng, Jun, Xiao Chen, Tianyu Zhu, and Yangyang Pan. "Data mining Approaches in Manpower Evaluation." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.115.

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Salankar, Suresh, Ameya Salankar, Atharva Sune, Prakhar Suryavansh, and Harsh Kumar. "Crop Suggestion using Data Mining Approaches." In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9579999.

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"Towards Online Data Mining System for Enterprises." In 7th International Conference on Evaluation of Novel Approaches to Software Engineering. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004098101870192.

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Oliveira, R. L., B. S. L. P. de Lima, and N. F. F. Ebecken. "A comparison of bio-inspired metaheuristic approaches in classification tasks." In DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070031.

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De Bruin, Jeroen, Tim Cocx, Walter Kosters, Jeroen J. Laros, and Joost Kok. "Data Mining Approaches to Criminal Career Analysis." In Sixth International Conference on Data Mining (ICDM'06). IEEE, 2006. http://dx.doi.org/10.1109/icdm.2006.47.

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Yoon, S. C., I. Y. Song, and E. K. Park. "Intensional query processing using data mining approaches." In the sixth international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/266714.266896.

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DZIUDA, DARIUS M. "DATA MINING APPROACHES TO MULTIVARIATE BIOMARKER DISCOVERY." In Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0008.

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Sundaravaradan, Naren, Manish Marwah, Amip Shah, and Naren Ramakrishnan. "Data mining approaches for life cycle assessment." In 2011 IEEE International Symposium on Sustainable Systems and Technology (ISSST). IEEE, 2011. http://dx.doi.org/10.1109/issst.2011.5936863.

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DEMIRCI, Mustafa, Bestami TAŞAR, Yunus Ziya KAYA, Ercan GEMİCİ, and Ercan GEMİCİ. "Monthly Groundwater Level Modeling Using Data Mining Approaches." In Air and Water – Components of the Environment 2021 Conference Proceedings. Casa Cărţii de Ştiinţă, 2021. http://dx.doi.org/10.24193/awc2021_07.

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Determination of the fluctuations in groundwater level (GWL) in terms of planning and operating their resources is important. In Turkey, many basins are experiencing problems in terms of the potential of groundwater. Increasing water demand, adverse conditions created by climate change and lack of planning related to underground water management in the basin have increased these problems. As a field of application, it was applied for General Directorate of State Hydraulic Works (DSI) well of Hatay province in Turkey. In the study, GWL predictions were evaluated using data mining approaches such as Radial Basis Neural Network (RBNN) and Support Vector Machines (SVM) methods. Monthly data sets between 2002 and 2015, including hydrological parameters predict the GWL used. According to comparison results, it was observed that the data mining models gave good results for observation in test phase.
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Reports on the topic "Data Mining Approaches"

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Lee, Wenke, and Salvatore J. Stolfo. Data Mining Approaches for Intrusion Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada401496.

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Durovic, Mateja, and Franciszek Lech. A Consumer Law Perspective on the Commercialization of Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64577.

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Commercialization of consumers’ personal data in the digital economy poses serious, both conceptual and practical, challenges to the traditional approach of European Union (EU) Consumer Law. This article argues that mass-spread, automated, algorithmic decision-making casts doubt on the foundational paradigm of EU consumer law: consent and autonomy. Moreover, it poses threats of discrimination and under- mining of consumer privacy. It is argued that the recent legislative reaction by the EU Commission, in the form of the ‘New Deal for Consumers’, was a step in the right direction, but fell short due to its continued reliance on consent, autonomy and failure to adequately protect consumers from indirect discrimination. It is posited that a focus on creating a contracting landscape where the consumer may be properly informed in material respects is required, which in turn necessitates blending the approaches of competition, consumer protection and data protection laws.
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Smith, III, Nguyen James F., and ThanhVu H. Evolutionary Data Mining Approach to Creating Digital Logic. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada524122.

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Lang, Chunmin, Li Zhao, and Muzhen Li. Understanding Consumers� Online Fashion Renting Experiences: A Data-Mining Approach. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8332.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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NORTH CAROLINA STATE UNIV AT RALEIGH. A Data Mining approach for building cost-sensitive and light intrusion detection models. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422555.

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Lin, Zongli, and Scott T. Acton. Research Area 5: Video Data Mining and Target Tracking: A Model Adaptation and Feedback Control Approach. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada605921.

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Pou, Jose, Jeff Duffany, and Alfredo Cruz. Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan: A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada581564.

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Ghanim, Murad, Joe Cicero, Judith K. Brown, and Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, February 2010. http://dx.doi.org/10.32747/2010.7592654.bard.

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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that interfere with whitefly-mediated begomovirus transmission. The project objectives were to: 1) Perform gene expression analyses using microarrays to study the response of whiteflies (B, Q and A biotypes) to the acquisition of begomoviruses (Tomato yellow leaf curl (TYLCV) and Squash leaf curl (SLCV). 2) Construct a whitefly proteome from whole whiteflies and dissected organs after begomovirus acquisition. 3) Validate gene expression by q-RTPCR and sub-cellular localization of candidate ESTs identified in microarray and proteomic analyses. 4) Verify functionality of candidate ESTs using an RNAi approach, and to link these datasets to overall functional whitefly anatomical studies. During the first and second years biological experiments with TYLCV and SLCV acquisition and transmission were completed to verify the suitable parameters for sample collection for microarray experiments. The parameters were generally found to be similar to previously published results by our groups and others. Samples from whole whiteflies and midguts of the B, A and Q biotypes that acquired TYLCV and SLCV were collected in both the US and Israel and hybridized to B. tabaci microarray. The data we analyzed, candidate genes that respond to both viruses in the three tested biotypes were identified and their expression that included quantitative real-time PCR and co-localization was verified for HSP70 by the Israeli group. In addition, experiments were undertaken to employ in situ hybridization to localize several candidate genes (in progress) using an oligonucleotide probe to the primary endosymbiont as a positive control. A proteome and corresponding transcriptome to enable more effective protein identification of adult whiteflies was constructed by the US group. Further validation of the transmission route of begomoviruses, mainly SLCV and the involvement of the digestive and salivary systems was investigated (Cicero and Brown). Due to time and budget constraints the RNAi-mediated silencing objective to verify gene function was not accomplished as anticipated. HSP70, a strong candidate protein that showed over-expression after TYLCV and SLCV acquisition and retention by B. tabaci, and co-localization with TYLCV in the midgut, was further studies. Besides this protein, our joint research resulted in the identification of many intriguing candidate genes and proteins that will be followed up by additional experiments during our future research. To identify these proteins it was necessary to increase the number and breadth of whitefly ESTs substantially and so whitefly cDNAs from various libraries made during the project were sequenced (Sanger, 454). As a result, the proteome annotation (ID) was far more successful than in the initial attempt to identify proteins using Uniprot or translated insect ESTs from public databases. The extent of homology shared by insects in different orders was surprisingly low, underscoring the imperative need for genome and transcriptome sequencing of homopteran insects. Having increased the number of EST from the original usable 5500 generated several years ago to >600,000 (this project+NCBI data mining), we have identified about one fifth of the whitefly proteome using these new resources. Also we have created a database that links all identified whitefly proteins to the PAVEdb-ESTs in the database, resulting in a useful dataset to which additional ESTS will be added. We are optimistic about the prospect of linking the proteome ID results to the transcriptome database to enable our own and other labs the opportunity to functionally annotate not only genes and proteins involved in our area of interest (whitefly mediated transmission) but for the plethora of other functionalities that will emerge from mining and functionally annotating other key genes and gene families in whitefly metabolism, development, among others. This joint grant has resulted in the identification of numerous candidate proteins involved in begomovirus transmission by B. tabaci. A next major step will be to capitalize on validated genes/proteins to develop approaches to interfere with the virus transmission.
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Kindt, Roeland, Ian K Dawson, Jens-Peter B Lillesø, Alice Muchugi, Fabio Pedercini, and James M Roshetko. The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21001.pdf.

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A systematic approach to tree planting and management globally is hindered by the limited synthesis of information sources on tree uses and species priorities. To help address this, the authors ‘mined’ information from 23 online global and regional databases to assemble a list of the most frequent tree species deemed useful for planting according to database mentions, with a focus on tropical regions. Using a simple vote count approach for ranking species, we obtained a shortlist of 100 trees mentioned in at least 10 of our data sources (the ‘top-100’ species). A longer list of 830 trees that were mentioned at least five times was also compiled. Our ‘top-100’ list indicated that the family Fabaceae (syn. Leguminosae) was most common. The information associated with our mined data sources indicated that the ‘top-100’ list consisted of a complementary group of species of differing uses. These included the following: for wood (mostly for timber) and fuel production, human nutrition, animal fodder supply, and environmental service provision (varied services). Of these uses, wood was most frequently specified, with fuel and food use also highly important. Many of the ‘top-100’ species were assigned multiple uses. The majority of the ‘top-100’ species had weediness characteristics according to ‘attribute’ invasiveness databases that were also reviewed, thereby demonstrating potential environmental concerns associated with tree planting that need to be balanced against environmental and livelihood benefits. Less than half of the ‘top-100’ species were included in the OECD Scheme for the Certification of Forest Reproductive Material, thus supporting a view that lack of germplasm access is a common concern for trees. A comparison of the ‘top-100’ species with regionally-defined tree inventories indicated their diverse continental origins, as would be anticipated from a global analysis. However, compared to baseline expectations, some geographic regions were better represented than others. Our analysis assists in priority-setting for research and serves as a guide to practical tree planting initiatives. We stress that this ‘top-100’ list does not necessarily represent tree priorities for the future, but provides a starting point for also addressing representation gaps. Indeed, our primary concern going forward is with the latter.
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