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IRobotSoft -- Visual Web Scraping and Web Automation Tool for FREE Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Weka supports deep learning! Gapminder XELOPES - prudsys The prudsys XELOPES (eXtEnded Library fOr Prudsys Embedded Solutions) is a platform and data source independent business intelligence library which unites classical data mining methods and new real time analytics. The library can be used as standalone software, offering pre-fabricated solutions to fundamental analytics problems; furthermore, it can be integrated into other software products, emphasising its full performance capacity as an embedded analytical tool. Especially when it comes to new and complex problems, the numerous algorithms of the prudsys XELOPES, which can be combined in modules, allow for the development of adequate solutions. Data mining standards prudsys XELOPES supports essential BI standards. Stream access Since classical data mining processes must generally handle extremely large data matrices, the streaming concept for data access was implemented in the prudsys XELOPES. Analytical functions The prudsys XELOPES combines a number of classical data mining models.

Stat eXplorer Interactive Statistical Visualization using Adobe Flash Statistics eXplorer integrates many common InfoVis and GeoVis methods required to make sense of statistical data, uncover patterns of interests, gain insight, tell-a-story and finally communicate knowledge. Statistics eXplorer was developed based on a component architecture and includes a wide range of visualization techniques enhanced with various interaction techniques and interactive features to support better data exploration and analysis. It also supports multiple linked views and integrated storytelling with a snapshot mechanism for capturing discoveries made during the exploratory data analysis process which can be used for sharing gained knowledge. The eXplorer applications are available on the NCVA/LiU web site for educational and research usage only. Learn more about eXplorer through these 2 videos: Introduction to eXplorer eXplorer Data Management Explore, present and communicate Read Paper about: Statistikatlas (SCB)

The R Project for Statistical Computing Fast Thinking and Slow Thinking Visualisation Last week I attended the Association of American Geographers Annual Conference and heard a talk by Robert Groves, Director of the US Census Bureau. Aside the impressiveness of the bureau’s work I was struck by how Groves conceived of visualisations as requiring either fast thinking or slow thinking. Fast thinking data visualisations offer a clear message without the need for the viewer to spend more than a few seconds exploring them. These tend to be much simpler in appearance, such as my map of the distance that London Underground trains travel during rush hour. The explicit message of this map is that surprisingly large distances are covered across the network and that the Central Line rolling stock travels furthest. or the seemingly impenetrable (from a distance at least), but wonderfully intricate hand drawn work of Steven Walter (click image for interactive version). So do the renowned folks at the NY Times Graphics Dept. prefer fast or slow thinking visualisations?

Interactive Dynamics for Visual Analysis Graphics Jeffrey Heer, Stanford University Ben Shneiderman, University of Maryland, College Park The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. Visualization provides a powerful means of making sense of data. The goal of this article is to assist designers, researchers, professional analysts, procurement officers, educators, and students in evaluating and creating visual analysis tools. Our focus on interactive elements presumes a basic familiarity with visualization design. Within each branch of the taxonomy presented here, we describe example systems that exhibit useful interaction techniques. Some visualization system designers have explored alternative approaches. 1.

Cytoscape: An Open Source Platform for Complex Network Analysis and Visualization

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