KEEL: Knowledge Extraction based on Evolutionary Learning

KEEL is a software tool to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. It contains a big collection of classical knowledge extraction algorithms, preprocessing techniques (instance selection, feature selection, discretization, imputation methods for missing values, etc.), Computational Intelligence based learning algorithms, including evolutionary rule learning algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...), and hybrid models such as genetic fuzzy systems, evolutionary neural networks, etc.

It allows to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. Moreover, KEEL has been designed with a double goal: research and educational.

SECABA²

SECABA², a web application that allows to capture library user opinion polls using the LibQUAL+^TM model. The polls allow the inclusion of several types of questions(diferent options, free text fields, minimum, desired and observed levels, and so on) and allows to group them into different categories that can be later aggregated and studied.

The application is able to perform some analysis with the polls data and to produce some different statistical graphs that can be later used to create satisfaction studies about the libraries. All the polls and reports generated by the application can be customized to the different needs of the library and adapted to both English and Spanish languages.

Web of Science Query Partitioner

WoS Query Partitioner is a tool that interactively splits a Web of Science query which returns more than 100,000 results into smaller queries to allow to easily obtain an exact result count.

It works by splitting the query using the Source field (SO) in a kind of Divide and Conquer recursive strategy.

In addition the application offers two different kinds of graphs which depict the partitioning process as well as a LaTeX table output of the executed queries.

RSNNS

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the RSNNS low-level interface, nearly all of the functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

SciMAT

SciMAT (Science Mapping Analysis software Tool) is an open source science mapping software tool which incorporates methods, algorithms, and measures for all the steps in the general science mapping workflow, from preprocessing to the visualization of the results. SciMAT allows the user the possibility of carrying out studies based on several bibliometric networks. Different normalization and similarity measures can be used over the data. Several clustering algorithms can be chosen to cut up the data. In the visualization module, three representations (strategic diagrams, cluster networks, and evolution areas) are jointly used, which allows the user to better understand the results.