The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform
Hongji Yang - One of the best experts on this subject based on the ideXlab platform.
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Unlocking the Power of OPNET Modeler: OPNET Modeler user interface
Unlocking the Power of OPNET Modeler, 1Co-Authors: Hongji YangAbstract:This chapter walks through graphic user interfaces of OPNET Modeler to help familiarize the reader with the modeler's basic operations. If you are already familiar with OPNET Modeler, its user interface and basic operations, you may ignore this chapter. The user interfaces described in this chapter include: Project Management Dialog, Modeler Preferences Dialog, OPNET Editors, Simulation Results Browser, and OPNET Documentation Browser. Project management OPNET projects can be easily managed in OPNET Modeler. In “File” menu, a user can choose to create a new project, open an existing project, delete a project, or add a model Directory, etc. To create and open a custom project within a Directory, you can follow these steps: Create a Directory where you want your OPNET model project files to be saved. For different projects, you may create individual directories. From OPNET Modeler, go to “File – Manage Model Files” menu, choose “Add Model Directory” to add the newly created Directory. Then you'll be prompted to confirm model Directory as shown in Figure 3.1. You can check both “Include all subdirectories” and “Make this the Default Directory” options. It is noted that “Make this the Default Directory” option will force files of other new projects to be saved in this Directory. Therefore, it is advisable to always select this option for a new project in order to save the files of the new project into a separate Directory. […]
Gary D. Miner - One of the best experts on this subject based on the ideXlab platform.
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The Three Most Common Data Mining Software Tools
Handbook of Statistical Analysis and Data Mining Applications, 2009Co-Authors: Robert Nisbet, John Elder, Gary D. MinerAbstract:This chapter introduces the interfaces of three of the common data mining tools on the market: SPSS Clementine, SAS-Enterprise Miner, and STATISTICA Data Miner. SPSS Clementine is the most mature among the major data mining packages on the market today. It enables one to quickly develop predictive models and deploy them in business processes to improve decision-making. The Clementine system looks for files in the Default Directory and includes the option to create SuperNodes, which are groups of nodes indicated by a SuperNode icon. The SAS-EM data mining process consists of a process flow diagram, which is a form of a graphical user interface, where one can add nodes, modify nodes, connect nodes with arrows for the direction of flow of the computations, modify nodes, and save the entire workspace as a data mining project. Advanced visualization tools can be used to create multidimensional histograms and graphically compare different algorithm models. STATISTICA Data Miner distinguishes between categorical and continuous variables, and dependent and predictor (independent variables). It includes a complete deployment engine for Data Miner solutions that comprises various tools. STATISTICA Data Miner contains various designated procedures in the (Node Browser) folders titled Classification and Discrimination, Regression Modeling and Multivariate Exploration, and General Forecaster and Time Series, to perform complex analyses with automatic deployment and cooperative and competitive evaluation of models.
Robert Nisbet - One of the best experts on this subject based on the ideXlab platform.
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The Three Most Common Data Mining Software Tools
Handbook of Statistical Analysis and Data Mining Applications, 2009Co-Authors: Robert Nisbet, John Elder, Gary D. MinerAbstract:This chapter introduces the interfaces of three of the common data mining tools on the market: SPSS Clementine, SAS-Enterprise Miner, and STATISTICA Data Miner. SPSS Clementine is the most mature among the major data mining packages on the market today. It enables one to quickly develop predictive models and deploy them in business processes to improve decision-making. The Clementine system looks for files in the Default Directory and includes the option to create SuperNodes, which are groups of nodes indicated by a SuperNode icon. The SAS-EM data mining process consists of a process flow diagram, which is a form of a graphical user interface, where one can add nodes, modify nodes, connect nodes with arrows for the direction of flow of the computations, modify nodes, and save the entire workspace as a data mining project. Advanced visualization tools can be used to create multidimensional histograms and graphically compare different algorithm models. STATISTICA Data Miner distinguishes between categorical and continuous variables, and dependent and predictor (independent variables). It includes a complete deployment engine for Data Miner solutions that comprises various tools. STATISTICA Data Miner contains various designated procedures in the (Node Browser) folders titled Classification and Discrimination, Regression Modeling and Multivariate Exploration, and General Forecaster and Time Series, to perform complex analyses with automatic deployment and cooperative and competitive evaluation of models.
John Elder - One of the best experts on this subject based on the ideXlab platform.
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The Three Most Common Data Mining Software Tools
Handbook of Statistical Analysis and Data Mining Applications, 2009Co-Authors: Robert Nisbet, John Elder, Gary D. MinerAbstract:This chapter introduces the interfaces of three of the common data mining tools on the market: SPSS Clementine, SAS-Enterprise Miner, and STATISTICA Data Miner. SPSS Clementine is the most mature among the major data mining packages on the market today. It enables one to quickly develop predictive models and deploy them in business processes to improve decision-making. The Clementine system looks for files in the Default Directory and includes the option to create SuperNodes, which are groups of nodes indicated by a SuperNode icon. The SAS-EM data mining process consists of a process flow diagram, which is a form of a graphical user interface, where one can add nodes, modify nodes, connect nodes with arrows for the direction of flow of the computations, modify nodes, and save the entire workspace as a data mining project. Advanced visualization tools can be used to create multidimensional histograms and graphically compare different algorithm models. STATISTICA Data Miner distinguishes between categorical and continuous variables, and dependent and predictor (independent variables). It includes a complete deployment engine for Data Miner solutions that comprises various tools. STATISTICA Data Miner contains various designated procedures in the (Node Browser) folders titled Classification and Discrimination, Regression Modeling and Multivariate Exploration, and General Forecaster and Time Series, to perform complex analyses with automatic deployment and cooperative and competitive evaluation of models.
Luo Fei - One of the best experts on this subject based on the ideXlab platform.
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Application of FTP on Linux
Computer and Modernization, 2003Co-Authors: Luo FeiAbstract:The paper discusses share of resource utilizing FTP server of Linux on LAN.Key technology accomplishing the target is to setup attribute of group,amends purview of provided with Directory and accessing Directory,amends Default Directory of entering FTP,and so on.
