Search Results for "data mining algorithms in c#.net" - Page 3

Genetic Programming (tree structure) predictor within Weka data mining software for both continuous and classification problems.

Openminer, as a data mining engine, is developed on java for analysis of dataset with the methods of data mining. By making use of openminer, we could discovery the knowledge which interests us but hides in the raw data.

JDMF is a data mining framework written in Java. Main features include: simplicity, flexibility, many algorithms to choose from, many formats of input (e.g. XML, CSV, JDBC, Java beans) and output data (e.g. XML, plain text info, charts).

KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.

basE91 is an advanced method for encoding binary data as ASCII characters. It is similar to UUencode or base64, but is more efficient. The overhead produced by basE91 depends on the input data. It amounts at most to 23% and can range down to 14%.

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.

SenseRank Sys: - builds the dictionaries (multidim matrices) of words’ values; - for the set utterance in certain language builds a figure in multidimensional space (in the matrix space) of values (visual schema), which is topological view of sense

musicomp is a program which most important element is an evolutionary algorithm which uses data mining methods as a fitness function to generate monophone melodies.

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.

Centre is a synthetic trajectory generator environment that aim to generate semantic-based trajectory datasets usable for spatio-temporal data-mining algorithms in testing and validation process.

Waters - the Waikato Analysis Tool for Events in Reactive Systems. This software will enable users to create finite-state machine models in a graphical user interface, to simulate their execution, and to apply model checking algorithms to them.

txtkit is a visual text mining tool for exploring large amounts of multilingual texts. It's an multiuser-application which mainly focuses on the process of reading and reasoning as series of decisions and events.

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.

Harvestman is a context aware metasearch engine which functions as a universal infromation gatherer and data mining system for the internet.

Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other

OpenGMP is an open service platform for implementing advanced decision support solutions for the mining enterprise.

reputron is a knowledge extraction engine platform that covers all aspect of text mining, relevance, indexing and querying on a corpus of text documents.

Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.

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