Conduct data mining for research and analysis purposes with the suite supporting multiple methods including exploratory data analysis, statistical learning, machine learning, etc. Multiple supervised algorithms such as clustering, factorial analysis, parametric and nonparametric statistics, association rule, feature selection are applied manually or automatically.
TANAGRA is a free DATA MINING software for academic and research purposes. It proposes several data mining methods from exploratory data analysis, statistical learning, machine learning and databases area. This project is the successor of SIPINA which implements various supervised learning algorithms, especially an interactive and visual construction of decision trees.
TANAGRA is more powerful, it contains some supervised learning but also other paradigms such as clustering, factorial analysis, parametric and nonparametric statistics, association rule, feature selection and construction algorithms
The main purpose of Tanagra project is to give researchers and students an easy-to-use data mining software, conforming to the present norms of the software development in this domain (especially in the design of its GUI and the way to use it), and allowing to analyse either real or synthetic data.
The second purpose of TANAGRA is to propose to researchers an architecture allowing them to easily add their own data mining methods, to compare their performances. TANAGRA acts more as an experimental platform in order to let them go to the essential of their work, dispensing them to deal with the unpleasant part in the programming of this kind of tools : the data management.
The third and last purpose, in direction of novice developers, consists in diffusing a possible methodology for building this kind of software. They should take advantage of free access to source code, to look how this sort of software is built, the problems to avoid, the main steps of the project, and which tools and code libraries to use for. In this way, Tanagra can be considered as a pedagogical tool for learning programming techniques.
v1.4 [Mar 5, 2008]
PRINCIPAL COMPONENT ANALYSIS. Additional outputs for the component: Scree plot and variance explained cumulative curve; PCA Correlation Matrix - Some outputs are provided for the detection of the significant factors (Kaiser-Guttman, Karlis-Saporta-Spinaki, Legendre-Legendre broken-stick test); PCA Correlation Matrix - Bartlett's sphericity test is performed and the Kaiser's measure of sampling adequacy (MSA) is calculated; PCA Correlation Matrix - The correlation matrix and the partial correlations between each pair of variables controlling for all other variables (the negative anti-image correlation) are produced.
PARALLEL ANALYSIS. The component calculates the distribution of eigenvalues for a set of randomly generated data. It proceeds by randomization. It applies to the principal components analysis and te multiple correspondence analysis. A factor is considered significant if its observed eigenvalue is greater than the 95-th percentile (this setting can be modified).
BOOTSTRAP EIGENVALUES. It calculates by bootstrap approach the confidence intervals of eigenvalues. A factor is considered significant if its eigenvalue is greater than a threshold which depends on the underlying factor method (PCA or MCA) method, or if the lower bound of the eigenvalue of a factor is greater than higher bound of the following one. The confidence level 0.90 can be modified. This component can be applied to the principal component analysis or the multiple correspondence analysis.
JITTERING. Jittering feature is incorporated to the scatter plot components (SCATTERPLOT, CORRELATION SCATTERPLOT, SCATTERPLOT WITH LABEL, VIEW MULTIPLE SCATTERPLOT).
RANDOM FOREST. The not used memory is released after the decision tree learning process. This feature is especially useful when we use an ensemble learning approach where we store a large number of trees in memory (BAGGING, BOOSTING, RANDOM FOREST). The memory occupation is reduced. The computation efficiency is improved.
Feature-rich relational database known for its high performance and excellent concurrency
