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  We provide projects on wireless communication, WEKA, SentiWordNet, SDN, QUALlNet, OPNET, OpenCV, MiniNet, PEERSIM and so on

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  We do research on Wordnet, Grid Computing, Cloud Computing, Computer Networking, etc

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  We provide electrical projects based on power electronics, MATLAB Simulink and SIM Power

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  For Electronics Engineering Students we support technologies like ARM, GSM, GPS, RFID, Robotics, VLSI, NSL, NS3, OMNet++, OPNet, QUALNET, PeerSim

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  For Computer Science Students we support NS2, NS3, MATLAB, Java, OMNeT++, OPNet, CloudSim, Qualnet, Weka, Wordnet, Dot Net, Hadoop,….

Rapid Miner projects is a platform for software environment to learn and experiment data mining and machine learning. We offer rapid miner final year projects to ensure optimum service for research and real world data mining process. We write rapid miner projects by java to discover knowledge and to construct operator tree. We use rapid miner to denote standardize interchange format of data mining process. We developed 90 rapid miner projects with full integrated environment for data mining, business intelligent, machine learning and predictive analytics applications. We provide rapid miner tool for data analysis to ensure code integration.

RAPIDMINER PROJECTS

Rapid Miner projects:

We use rapid miner which contains GUI, to interconnect user requested operation with processing operations and implemented in B.Tech projects. It ensures a direct output with rapid miner environment. It composed of java API to integrate data mining environment (or) business intelligent code to java processing code.

Benefits of Rapid Miner Tool:

 We adopts following benefits from Science Direct reference papers are:

• Efficient data transformation method such as aggregation, normalization, filters, sampling, discretization and missing value replacements are performed.

• Data mining model are ensured for research and development of data mining applications.

• Support evaluation technique such as leave one out, back testing, cross validation, significance test, sliding widow method, and region of interest.

• Enhance various algorithms such as decision tree, bayes theorem, boosting, clustering,Aproiori, rule learners are implemented.

Rapid Miner Versions:

We support two types of rapid miner version based on students interest they are community and enterprise, community version is an open source version. Enterprise is an advance feature of community and it also contains services and provides guarantees.

Properties in Rapid Miner:

 We promote following properties in our project are:

• Efficient data handling mechanism are used for multilayered data viewing concepts.
• Composed of java API, GUI, command line mode.
• Developed by java language.
• For large scale high dimension data it provides plotting facility.

RAPIDMINER PROJECT

Application Area in Rapid Miner:

 We implement following application in rapid miner are

• Feature Selection Engineering.
• Data Mining.
• Distributed Data Mining.
• Multimedia Mining.
• Data Stream Mining.
• Text Mining.
• Tracking Drifting Concepts.

Steps Involved In Rapid Miner Tool:

 We follow these steps in Rapid miner tool are:

• Assign Input Data Set File:

            It selects ARFF File format (or) other supported file format data set as input.

• Select Functionality:

            Based on user need it select function such as classification, feature selection and other function enhanced by rapid miner environment.

• Execute Function:

          Execute a given input data set with user defined function and provide output in rapid miner environment.