Search Results for "data mining algorithms in c#.net"

MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values.

CoRoPa stands for Computational Rough Paths. The aim of CoRoPa is to provide a software framework for various ideas related to Rough Path Theory, including rough differential equations and the digital description of serial data streams.

The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).

math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net

DRAMMS is a software package designed for 2D-to-2D and 3D-to-3D deformable medical image registration tasks. Released by Section of Biomedical Image Analysis (SBIA) at the University of Pennsylvania. Some typical applications of DRAMMS include, -- Cross-subject registration of the same organ (can be brain, breast, cardiac, etc); -- Mono- and Multi-modality registration (MRI, CT, histology); -- Longitudinal registration (pediatric brain growth, cancer development, mouse...

Graph-RAT is a database abstraction layer designed to make it easy to use a large library of graph-analysis routines on a database as well as add new kinds of algorithms to data mining.

This project aims to be a easy-to-use toolkit of algorithms and utilities for semantic data mining. So far all algorithms are implemented as web services and we provide widgets for their use in the Orange4WS data mining platform.

Ferda is a user friendly data mining tool. It is a modular distributed multiplatform framework based on Internet Communications Engine. Ferda is very powerful in working with association rules.

Open data mining platform. Provides common architecture for algorithms of various types. Efficient processing of arbitrarily large volumes of data thanks to data streaming. Weka and Rseslib partially integrated. (www.debellor.org)

PIAF (Prototype Image Applications Fast) is a prototyping tool for helping development of image processing algorithms. Based on a Qt GUI, it can display and process several live video inputs or still images with a plugins based architecture.

A suite of data mining algorithms implemented in C++ for use in a distributed environment.

The package implements a variety of tools for categorization of multivariate data such as boosted decision trees, bagging and random forest, bump hunting (PRIM), a multi-class learner and others.

The aim of the project is to provide open source collection of algorithms in the field of spectroscopy: data handling and processing, modeling and artificial intelligence tools.

Fast, scriptable data mining application. Supports unix pipes, redirection.

Data mining tool for the extraction of spatio-temporal frequent patterns ("Trajectory patterns" or "T-patterns") from GPS-like trajectories of a set of moving objects. Work performed within the European project GeoPKDD - www.geopkdd.eu

baobab is an implementation of FPTrees or Frequent Pattern Trees, a pattern recognition/data mining technique. it has innumerable applications in language processing, clickstream analysis, etc.

This project intends to create an indexing search engine, for knowledge management. The primary object is to apply an information retrieval core. And implement a knowledge data discovery theory such as data mining algorithm, text mining.

This project provides visual interactive interfaces to custom algorithms used in genomes research.

JMiner is a (not yet!) complete data mining and artificial intelligence solution written in Java. Support for neural networks, genetic algorithms/programming, decision trees, clustering, market basket analysis, link analysis, data cleansing, and others.

Incridge - A Software Tool for Scalable, Parallel, Incremental and Decremental Classification based on Support Vector Machine (SVM) Approximation Algorithms. Possible use include: web usage mining, bioinformatics and spam-classification.

ai applications,or 'aiapps', is a suite of applications applying algorithms and methods from the domain/field of artificial intelligence, machine learning, evolutionary computation, neural nets etc....to real world problems

Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.

Weka++ is a collection of machine learning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.

This project collect several data mining methods, such as: 1) clustering methods; 2) biclustering methods; 3) FCA methods and etc.