Weka

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the development of algorithms that take as input empirical data, such as that from sensors ordatabases. The algorithm is designed to (a) identify (i.e., quantify) complex relationships thought to be features of the underlying mechanism that generated the data, and (b) employ these identified patterns to make predictions based on new data. Data can be seen as instances of the possible relations between observed variables; the algorithm acts as a machine learner which studies a portion of the observed data (called examples of the data or training data) to capture characteristics of interest of the data's unknown underlying probability distribution, and employs the knowledge it has learned to make intelligent decisions based on new input data.^[1]

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. It is also well-suited for developing new machine learning

schemes.

For more information visit

http://www.cs.waikato.ac.nz/ml/weka/