Conferences related to Data preprocessing

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2015 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

IPDPS is an international forum for engineers and scientists from around the world to present their latest research findings in all aspects of Parallel Processing.

  • 2013 IEEE International Symposium on Parallel & Distributed Processing (IPDPS)

    Parallel and distributed algorithms, focusing on stability, scalability, and fault-tolerance. Applications of parallel and distributed computing, including web, peer-to-peer, cloud, grid, scientific, and mobile computing. Parallel and distributed architectures including instruction-level and thread-level parallelism; petascale and exascale systems designs. Parallel and distributed software, including parallel and multicore programming languages, compilers, runtime systems, operating systems, and middleware for grids and clouds.

  • 2011 IEEE International Parallel & Distributed Processing Symposium (IPDPS)

    IPDPS is an international forum for engineers and scientists from around the world to present their latest research findings in all aspects of parallel computation. In addition to technical sessions of submitted paper presentations, the meeting offers workshops, tutorials, and commercial presentations & exhibits. IPDPS represents a unique international gathering of computer scientists from around the world.

  • 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS)


2013 IEEE 11th International Conference on Industrial Informatics (INDIN)

The aim of the conference is to bring together researchers and practitioners from industry and academia and provide them with a platform to report on recent developments, deployments, technology trends and research results, as well as initiatives related to industrial informatics and their application.


2012 4th Conference on Data Mining and Optimization (DMO)

The scope of the conference includes, but is not limited to the following subjects: Parallel and distributed data mining algorithms, Data streams mining, Graph mining, Spatial data mining, Text & multimedia mining, Web mining, Pre-processing techniques, etc. Linear/Nonlinear Optimization, Integer/Combinatorial Optimization, metaheuristics, Network Optimization, Scheduling Problems and Stochastic Optimization.

  • 2011 3rd Conference on Data Mining and Optimization (DMO)

    Data and text mining tasks such as classification, prediction, clustering, association rules mining, etc. Data mining techniques such neural networks, genetic algorithm, artificial immune system, etc. Automated scheduling and planning models, heuristics and algorithms. Optimization problems including scheduling, timetabling, manufacturing, logistics, space allocation, anomaly detection, bioinformatics, etc.

  • 2009 2nd Conference on Data Mining and Optimization (DMO 2009)

    Data & text mining tasks such as classification, prediction, clustering, etc. Data & text mining techniques such as neural networks, genetic algorithm and other soft computing technique. Data & text Mining Applications in Medical, Healthcare and other fields. Optimization Techniques for Data & text mining. Optimization algorithms such as Local Search, Meta-heuristics search, Heuristic Search and others. Application of oprimizations technique such as in Shop-floor scheduling, Sport scheduling, Timetablin


2012 4th International Conference on Intelligent & Advanced Systems (ICIAS)

Sensor Technology Nonlinear Circuits & Systems Signal Processing Instrumentation & Control Systems Communications Systems Image Processing & Multimedia Systems Biomedical Systems VLSI & Embedded Systems Power Electronics & Power Systems Computational & Articial Intelligence

  • 2010 International Conference on Intelligent & Advanced Systems (ICIAS)

    Theory & Systems - Neural Networks & Systems - Artificial Intelligence - Computational Method - Non-linear Circuits & Systems - Signal Processing - Wavelet & Filter Banks Analog & Digital Systems - Sensory & Control Systems - Communication Systems - Image Processing & Multimedia Systems - VLSI & Embedded Systems - Biomedical Systems - Power Electronic & Power Systems

  • 2007 International Conference on Intelligent & Advanced Systems (ICIAS)

    ICIAS 2007 aims at bringing together experts and researchers working in the area of advanced and intelligent systems. Last few decades have seen proliferation of many kind of systems due mainly to advancement in theory, analysis and design techniques of circuits and systems. These systems have found applications in biomedicine, communication engineering, giga-scale systems, nanotechnology and power electronics.


2012 IEEE 13th International Conference on Information Reuse & Integration (IRI)

Given volumes of information in digital form, we are constantly faced with new challenges with regards to efficiently using it and extracting useful knowledge from it. Information reuse and integration (IRI) seeks to maximally exploit such available information to create new knowledge and to reuse it for addressing newer challenges. It plays a pivotal role in the capture, maintenance, integration, validation, extrapolation, and application of knowledge to augment human decision -making capabilities.

  • 2011 IEEE International Conference on Information Reuse & Integration (IRI)

    Given volumes of information in digital form, we are constantly faced with new challenges with regards to efficiently using it and extracting useful knowledge from it. Information reuse and integration (IRI) seeks to maximally exploit such available information to create new knowledge and to reuse it for addressing newer challenges. It plays a pivotal role in the capture, maintenance, integration, validation, extrapolation, and application of knowledge to augment human decision -making capabilities.

  • 2010 IEEE International Conference on Information Reuse & Integration (2010 IRI)

    Given volumes of information in digital form, we are constantly faced with new challenges with regards to efficiently using it and extracting useful knowledge from it. Information reuse and integration (IRI) seeks to maximally exploit such available information to create new knowledge and to reuse it for addressing newer challenges. It plays a pivotal role in the capture, maintenance, integration, validation, extrapolation, and application of knowledge to augment human decision -making capabilities.


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Periodicals related to Data preprocessing

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Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...


Parallel and Distributed Systems, IEEE Transactions on

IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. Topic areas include, but are not limited to the following: a) architectures: design, analysis, and implementation of multiple-processor systems (including multi-processors, multicomputers, and networks); impact of VLSI on system design; interprocessor communications; b) software: parallel languages and compilers; scheduling and task partitioning; databases, operating systems, and programming environments for ...


Potentials, IEEE

This award-winning magazine for technology professionals explores career strategies, the latest research and important technical developments. IEEE Potentials covers theories to practical applications and highlights technology's global impact.



Most published Xplore authors for Data preprocessing

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Xplore Articles related to Data preprocessing

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MSPtool: A Versatile Tool for Mass Spectrometry Data Preprocessing

Francesco Gullo; Giovanni Ponti; Andrea Tagarelli; Giuseppe Tradigo; Pierangelo Veltri Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on, 2008

Preprocessing mass spectrometry (MS) data has been recognized as a crucial preliminary phase in order to perform data management and knowledge discovery tasks on mass spectra. The huge dimensionality and heterogeneity of MS data make mandatory the use of tools that are able to guide the user in the MS preprocessing task. However, most MS preprocessing tools are typically designed ...


Contingency screening for steady-state security analysis by using FFT and artificial neural networks

T. S. Sidhu; Lan Cui IEEE Transactions on Power Systems, 2000

A new approach based on artificial neural networks (ANNs) combined with fast Fourier transform (FFT) is developed for single line contingency screening in steady-state security analysis. The offline fast decoupled load flow calculations are adopted to construct two kinds of performance indices, PIp (active power performance index) and PIv (reactive power performance index) which reflect the severity degree of contingencies. ...


Handwritten alphanumeric character recognition by the neocognitron

K. Fukushima; N. Wake IEEE Transactions on Neural Networks, 1991

A pattern recognition system which works with the mechanism of the neocognitron, a neural network model for deformation-invariant visual pattern recognition, is discussed. The neocognition was developed by Fukushima (1980). The system has been trained to recognize 35 handwritten alphanumeric characters. The ability to recognize deformed characters correctly depends strongly on the choice of the training pattern set. Some techniques ...


Direct Kalman filtering approach for GPS/INS integration

Honghui Qi; J. B. Moore IEEE Transactions on Aerospace and Electronic Systems, 2002

We present a novel Kalman filtering approach for GPS/INS integration. In the approach, GPS and INS nonlinearities are preprocessed prior to a Kalman filter. The GPS preprocessed data are taken as measurement input, while the INS preprocessed data are taken as additional information for the state prediction of the Kalman filter. The advantage of this approach, over the well-studied (extended) ...


Fault Diagnosis of Lead-Zinc Smelting Furnace based on Multi-Class Support Vector Machines

Shaohua Jiang; Weihua Gui; Chunhua Yang; Yongfang Xie 2007 IEEE International Conference on Control and Automation, 2007

Support vector machine (SVM) is powerful for the problem with small sampling, nonlinear and high dimension. A multi-class SVM classifier is applied to fault diagnosis of imperial smelting furnace in this paper. The input data is preprocessed through a special method based on the data reliability analysis technology, and six features are extracted as the input to multiple fault classifier ...


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Educational Resources on Data preprocessing

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eLearning

MSPtool: A Versatile Tool for Mass Spectrometry Data Preprocessing

Francesco Gullo; Giovanni Ponti; Andrea Tagarelli; Giuseppe Tradigo; Pierangelo Veltri Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on, 2008

Preprocessing mass spectrometry (MS) data has been recognized as a crucial preliminary phase in order to perform data management and knowledge discovery tasks on mass spectra. The huge dimensionality and heterogeneity of MS data make mandatory the use of tools that are able to guide the user in the MS preprocessing task. However, most MS preprocessing tools are typically designed ...


Contingency screening for steady-state security analysis by using FFT and artificial neural networks

T. S. Sidhu; Lan Cui IEEE Transactions on Power Systems, 2000

A new approach based on artificial neural networks (ANNs) combined with fast Fourier transform (FFT) is developed for single line contingency screening in steady-state security analysis. The offline fast decoupled load flow calculations are adopted to construct two kinds of performance indices, PIp (active power performance index) and PIv (reactive power performance index) which reflect the severity degree of contingencies. ...


Handwritten alphanumeric character recognition by the neocognitron

K. Fukushima; N. Wake IEEE Transactions on Neural Networks, 1991

A pattern recognition system which works with the mechanism of the neocognitron, a neural network model for deformation-invariant visual pattern recognition, is discussed. The neocognition was developed by Fukushima (1980). The system has been trained to recognize 35 handwritten alphanumeric characters. The ability to recognize deformed characters correctly depends strongly on the choice of the training pattern set. Some techniques ...


Direct Kalman filtering approach for GPS/INS integration

Honghui Qi; J. B. Moore IEEE Transactions on Aerospace and Electronic Systems, 2002

We present a novel Kalman filtering approach for GPS/INS integration. In the approach, GPS and INS nonlinearities are preprocessed prior to a Kalman filter. The GPS preprocessed data are taken as measurement input, while the INS preprocessed data are taken as additional information for the state prediction of the Kalman filter. The advantage of this approach, over the well-studied (extended) ...


Fault Diagnosis of Lead-Zinc Smelting Furnace based on Multi-Class Support Vector Machines

Shaohua Jiang; Weihua Gui; Chunhua Yang; Yongfang Xie 2007 IEEE International Conference on Control and Automation, 2007

Support vector machine (SVM) is powerful for the problem with small sampling, nonlinear and high dimension. A multi-class SVM classifier is applied to fault diagnosis of imperial smelting furnace in this paper. The input data is preprocessed through a special method based on the data reliability analysis technology, and six features are extracted as the input to multiple fault classifier ...


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IEEE-USA E-Books

  • References

    The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

  • Index

    The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

  • A Comparision of RBF and MLP Networks for Classification of Biomagnetic Fields

    This chapter contains sections titled: Introduction, The Problem, Model Assumptions, Production of Training Data, Preprocessing, Probabilistic Background, The Neural Network Topologies, Knowledge Extraction, Conclusion

  • Discovery of Patterns in Earth Science Data Using Data Mining

    This chapter contains sections titled: Introduction Data Description and Data Sources Data Preprocessing Clustering Association Analysis Query Processing Other Techniques Conclusions This chapter contains sections titled: Acknowledgments References

  • Generalized Maximum Margin Clustering and Unsupervised Kernel Learning

    Maximum margin clustering was proposed lately and has shown promising performance in recent studies [1, 2]. It extends the theory of support vector machine to unsupervised learning. Despite its good performance, there are three major problems with maximum margin clustering that question its efficiency for real-world applications. First, it is computationally expensive and difficult to scale to large-scale datasets because the number of parameters in maximum margin clustering is quadratic in the number of examples. Second, it requires data preprocessing to ensure that any clustering boundary will pass through the origins, which makes it unsuitable for clustering unbalanced dataset. Third, it is sensitive to the choice of kernel functions, and requires external procedure to determine the appropriate values for the parameters of kernel functions. In this paper, we propose "generalized maximum margin clustering" framework that addresses the above three problems simultaneously. The new framework generalizes the maximum margin clustering algorithm by allowing any clustering boundaries including those not passing through the origins. It significantly improves the computational efficiency by reducing the number of parameters. Furthermore, the new framework is able to automatically determine the appropriate kernel matrix without any labeled data. Finally, we show a formal connection between maximum margin clustering and spectral clustering. We demonstrate the efficiency of the generalized maximum margin clustering algorithm using both synthetic datasets and real datasets from the UCI repository.

  • Class Imbalance Learning Methods for Support Vector Machines

    Support vector machines (SVMs) is a very popular machine learning technique, which has been successfully applied to many real-world classification problems from various domains. Despite of all its theoretical and practical advantages, SVMs could produce suboptimal results with imbalanced datasets. This chapter briefly reviews the learning algorithm of SVMs. It discusses why SVMs are sensitive to the imbalance in datasets. The chapter also reviews the methods found in the literature to handle the class imbalance problem for SVMs. These methods have been developed as both data preprocessing methods (called external methods) and algorithmic modifications to the SVM algorithm (called internal methods). Fuzzy SVMs for Class Imbalance Learning (FSVM-CIL) settings have resulted in better classification results on the datasets than the existing CIL methods applied for standard SVMs, namely random oversampling, random undersampling, synthetic minority oversampling technique (SMOTE), different error costs (DEC), and zSVM methods.

  • Algorithmic Methods for the Analysis of Gene Expression Data

    The traditional approach to molecular biology consists of studying a small number of genes or proteins that are related to a single biochemical process or pathway. A major paradigm shift recently occurred with the introduction of gene-expression microarrays that measure the expression levels of thousands of genes at once. These comprehensive snapshots of gene activity can be used to investigate metabolic pathways, identify drug targets, and improve disease diagnosis. However, the sheer amount of data obtained using high throughput microarray experiments and the complexity of the existing relevant biological knowledge is beyond the scope of manual analysis. Thus, the bioinformatics algorithms that help analyze such data are a very valuable tool for biomedical science. First, a brief overview of the microarray technology and concepts that are important for understanding the remaining sections are described. Second, microarray data preprocessing, an important topic that has drawn as much attention from the research community as the data analysis itself is discussed. Finally, some of the more important methods for microarray data analysis are described and illustrated with examples and case studies.



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