The power of open-source development turns RapidMiner into one of the most widely used data mining and predictive analysis solutions world-wide. On the other hand, the company Rapid-I controls and guides the development and ensures that RapidMiner also is an enterprise-capable solution. Learn more about how the Enterprise Edition of RapidMiner can help you to get better results in faster times.
The purpose of Data.gov is to increase public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government.
This document describes the implementation of a DAQ model. It provides a number of tools to develop a data acquisition system.
To facilitate comunication between different objects, DAQ++ implements a very simple Observer model, in which some of the DAQ++ objects are defined as DAQpp::Observables and some as DAQpp::Observers. Observers subscribe to the messages defined in the Observables and are notified whenever a change occurs.
The basis of the system is the DAQpp::Module object. It represents a detector or DAQ unit. As such, it implements the basic DAQ commands to get ready, start or stop the DAQ, retreive the data, etc.
The Open Text Mining Interface (OTMI) is an initiative from Nature Publishing Group (NPG). It aims to enable scholarly publishers, among others, to disclose their full text for indexing and text-mining purposes but without giving it away in a form that is
Weka 3: Data Mining Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Powerful Search Engine designed for Document Management, Competitive Intelligence, Press Analysis and Text Mining, Web Mining, Knowledge Discovery, Strategic Watch...Has Report Writer, Web Spider, Publisher, more...
J. Abowd, L. Vilhuber, and W. Block. Privacy in Statistical Databases, volume 7556 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2012)
I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler. KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page 935--940. New York, NY, USA, ACM, (2006)