Tinn-R | Free Development software downloads Converting the Enterprise to an Adaptive Neural Network « Kyield Those tracking business and financial news may have observed that a little bit of knowledge in the corner office about enterprise architecture, software, and data can cause great harm, including for the occupant, often resulting in a moving van parked under the corner suite of corporate headquarters shortly after headlines on their latest preventable crisis. Exploitation of ignorance in the board room surrounding enterprise computing has become mastered by some, and is therefore among the greatest of many challenges for emerging technology that has the capacity for significant improvement. The issues surrounding neural networks requires total immersion for extended duration. Since many organizations lack the luxury of time, let’s get to it. Beware the Foreshadow of the Black Swan A recent article by Reuters confirms what is perhaps the worst kept secret in the post printing press era: Many Wall Street executives say wrongdoing is necessary: survey. Tailored Blueprint Like this:
Weka 3 - Data Mining with Open Source Machine Learning 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. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013.
Eclipse IDE for R Background: Eclipse is an open source Integrated Development Environment (IDE). As with Microsoft's Visual Studio product, Eclipse is programming language-agnostic and supports any language having a suitable plugin for the IDE platform. For Eclipse, the R language plugin is StatET. Figure 1 (above): Eclipse, StatET with R, and the R debugger (bottom window) at work. The following three (3) part procedure installs Eclipse onto a Windows platform (XP or Windows 7) and adds StatET (R) language support. Part-I Install Eclipse- Download the latest stable Eclipse release (I use Eclipse Classic which is at version 3.5.2 (163 MB) as of 10-April-2010). NOTES: your system may already have Java installed, in which case you can skip these Java installation steps. 64-bit Java runtime environments (JREs) are designated explicitly on the java.com website as 64-bit, for example 'Windows 7/XP/Vista/2003/2008 (64-bit)' or similar. Part-II Install StatET- Part-III Configure the StatET Eclipse plugin- Testing
Anametrix You want to make better use of data to improve all forms of consumer interactions, from campaign performance and social engagement to web site content and ad planning. It’s the key to determining whether your marketing decisions lead to success. But your data is trapped in dozens of systems, databases, spreadsheets and applications – both inside and outside your organization. Sound familiar? Unify Your Data Anametrix enables marketers like you to bring together and make sense of all your data, so you can focus your time on the analysis that will drive marketing performance. Turn Data into Insights Our cloud-based analytics platform gives you a unified view of your paid-, owned- and earned-media effectiveness to assess marketing effectiveness. With Anametrix, you can: Drive Revenue and Profitability By collecting, analyzing and making sense out of data from virtually any source, Anametrix delivers not just another set of dashboards, but a real decision-support solution.
GGobi data visualization system. RForge.net - development environment for R package developers An Efficient Density based Improved K- Medoids Clustering algorithm An efficient density based improved k-medoids clustering algorithm seminar topic explains about extracting information from raw data using clustering methods. In order to extract information from raw data kmedoids is the basic method used. Though they are easy to implement but they are many drawbacks in these methods. In order to overcome these drawbacks we propose a density based k-medois clustering method which performs better than DBSCAN in terms of quality. In this paper students can find detailed explanation on advantages of DBSCAN, disadvantages of DBSCAN, evaluation and results, conclusion. For more information on this topic students can download reference material from below link. Computer science and information technology students can find related projects, seminar topics , projects with source code from this site for free download. download An Efficient Density based Improved K- Medoids Clustering algorithm related information from this link. Custom Search