43 resources related to Data preprocessing
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2014 IEEE International Symposium on 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.
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.
2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)
Information reuse and integration (IRI) seeks to maximally exploit available digital information to create new knowledge and to reuse it for addressing newer challenges.
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.
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
Gao Qiang; Wang Xinmin; Dong Chao; Li Chenguang Computer Application and System Modeling (ICCASM), 2010 International Conference on , 2010
The water quality data in some petrochemical company are stored in the lims database, in order to support the field operation's decision-making according to these data, it's necessary to do some appropriate data mining. However, the accuracy of the results of data mining directly associated with the quality of source data, so data preprocessing on the raw data is necessary ...
Xishui Pan; Xuezhong Zhou; Hongmei Song; Runshun Zhang; Tingting Zhang e-Health Networking, Applications and Services (Healthcom), 2012 IEEE 14th International Conference on , 2012
Clinical data warehouse has been developed as a fundamental data infrastructure for large scale TCM clinical data management and decision support services. However, as a key component, data extraction, transforming and loading (ETL) is a complicated and labor intensive task to ensure high data quality before all kinds of data analyses. This paper introduces an enhanced ETL technique framework, which ...
Bakar, A.A.; Othman, Z.A.; Mohd Shuib, Nor Liyana Data Mining and Optimization, 2009. DMO '09. 2nd Conference on , 2009
Data preprocessing is an important step in data mining. It is used to resolve various types of problem in a large dataset in order to produce quality data. It consists of four steps, namely, data cleaning, integration, reduction and transformation. The literature shows that each preprocessing step consists of various techniques. In order to develop quality data, a data miner ...
Pirenne, B.; Guillemot, E. Oceans, 2012 , 2012
The advent of large scale, high-bandwidth on-line access and generously powered ocean floor observatories were predicted to transform the way ocean science is conducted. They have lived up to this promise. New cabled observatories have sometimes in excess of 100 different instruments semipermanently installed in areas of scientific interest. The instruments represent hundreds of different variables measured at a high ...
Othman, Z.A.; Bakar, A.A.; Hamdan, A.R.; Omar, K.; Mohd Shuib, Nor Liyana Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on , 2007
The current data mining tools is used to build knowledge based on a huge historical data. At present, businesses are facing with fast growing data that are very valuable in contributing knowledge. Knowledge should be updated regularly in order to ensure its quality and precision thus improve the decision making process. Data mining has shown great potential in extracting valuable ...
No jobs are currently tagged "Data preprocessing"
ACADIS: brokering arctic data for research
Richard Schreier Ph.D: Understanding Delta Sigma Data Converters
IEEE Themes - Learning about human behavior from mobile phone data
NREL Wind Technology Center
GEOSS for BIODIVERSITY -A demonstration of the GEOSS Common Infrastructure capabilities
WIE ILC: Leadership Q & A with Jaya Kolhatkar of WalmartLabs
Technical Tours: Eaton
IMS 2014 Opening Plenary
Approaches towards energy-efficiency in the cloud for emerging markets
Prospects and Challenges for GHz to THz Technologies/Architectures for Future Wireless Communications pt.2
Prospects and Challenges for GHz to THz Technologies/Architectures for Future Wireless Communications pt.1
Trends in Image, Video, and Multidimensional Signal Processing
WIE ILC: Exception to Expectation: Women in Engineering
IMS 2012 Steve Mollenkopf Plenary
Group on Earth Observations(GEOSS): Applications
Open Source Software: Opportunities for Social Innovation from Around the World
IEEE GLOBECOM 2010
APEC 2011- Methode Electronics at APEC 2011
NIKSUN World Wide Security & Mobility Conference 2011-Parag Pruthi
No standards are currently tagged "Data preprocessing"
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 ...
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 ...
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