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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Explored research on data preprocessing and mining technology for clinical data applications

Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on, 2010

On the basis of introducing data preprocessing and mining technology, research is developed on clustering and modeling of mining about clinical data (biochemical indicators), to find potential information related with health assessment and disease prediction, and to indicate further research direction. Based on characteristics of clinical data, Sigmoid function is used to preprocess the original data, then the self-organizing neural ...


Land use analysis of remote sensing data by Kohonen nets

Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International, 1997

The neural network approach by the backpropagation method (BPM) to land-use has been discussed in recent years. Using such a method, the accuracy depends on a training data set which has been selected manually. It takes much time to select a suitable training data set. In this paper, as a preprocessing of classifications the authors use the Kohonen feature map ...


Mining transcriptional association rules from breast cancer profile data

Information Reuse and Integration (IRI), 2011 IEEE International Conference on, 2011

To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing and two different association rule ...


Data Preprocessing Method of ECG Indicators When Applied ECG in Driver Mental Workload Research

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on, 2009

Electrocardiogram (ECG) indicators are widely used in driver mental workload studies. Because ECG is always recorded continuously in experiments, ECG indicator data often have trend. To make follow-up data analysis faster and more reliable, ECG indicator data should be preprocessed to remove or extract the trend according to the aim of the experiment. However, most researchers tend to ignore this ...


Arachne: a portable threads system supporting migrant threads on heterogeneous network farms

Parallel and Distributed Systems, IEEE Transactions on, 1998

We present the design and implementation of Arachne, a threads system that can be interfaced with a communications library for multithreaded distributed computations. In particular, Arachne supports thread migration between heterogeneous platforms, dynamic stack size management, and recursive thread functions. Arachne is efficient, flexible, and portable-it is based entirely on C and C++. To facilitate heterogeneous thread operations, we have ...


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Educational Resources on Data preprocessing

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eLearning

SSCM-Based Simulation of Stock Market: Put Bounded Rational into Social and Stock Environment

Nan Lin; Xiaochao Ding; Zheng Qin 2010 Third International Joint Conference on Computational Science and Optimization, 2010

In this paper, we classify stock simulation environments as cognitive environment, social environment and stock environment. Stock market simulation systems are usually built based on cognitive and social environment, the simulation results are affected by the mental factors and their interaction results. Based on the past researches, this paper presents a Social Stock Competition Model (SSCM) which takes stock environment ...


A Fuzzy Method for Signal Smoothing

Wenjing Yang; Yuanguo Zhu 2010 Third International Joint Conference on Computational Science and Optimization, 2010

In signal testing procedure, due to the various interference, experimental data records collected by test system always deviate from original true data. The interference factors do not only contain measurement errors but also contain ambiguity of measuring concepts. Mixed data makes contiguous sample values be mixed up and also makes data curves vibrate strongly, which will produce unexpected sharp signals. ...


Multi-output regularized projection

K. Yu; S. Yu; V. Tresp 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005

Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this ...


Handling preprocessor-conditioned declarations

L. Aversano; M. Di Penta; I. D. Baxter Proceedings. Second IEEE International Workshop on Source Code Analysis and Manipulation, 2002

Many software systems are developed with configurable functionality, and for multiple hardware platforms and operating systems. This can lead to thousands of possible configurations, requiring each configuration-dependent programming entity or variable to have different types. Such configuration-dependent variables are often declared inside preprocessor conditionals (e.g., C language). Preprocessor-conditioned declarations may be a source of problems. Commonly used configurations are type-checked ...


Analysis of Electrical Interference from Power Lines to Gas Pipelines Part I: Computation Methods

F. P. Dawalbi; R. D. Southey IEEE Power Engineering Review, 1989

Analysis of electrical interference effects of transmission lines upon nearby pipelines has been a topic of growing interest due to the proliferation of rights-of-way which must be shared by transmission lines and pipelines. This paper describes the results of a recent joint EPRI/A.G.A. research project whose objectives were to develop an effective tool for simulating complex realistic right-ofway problems accurately ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • 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.

  • 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.

  • 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.

  • 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

  • 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.

  • 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



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