View source for Algorithms for mining sparse data sets

The main purpose of this project is to re-design and re-implement some
classical machine learning and data mining algorithms to operate on
sparse data sets. The algorithms we consider are: NaiveBayes, ID3, C4.5,
M5Prime, LinearRegression, AdaBoost, Perceptron and Decision Stumps.
We developed a toolbox that can process sparse data sets and apply
selected algorithms to them. This toolbox is built in C. The name of the
toolbox is Sparse Data Miner, SDM. We performed numerous tests comparing
the speed, memory requirements and accuracy of our implementation
against the well-known WEKA system. Also some tests in the field of
recommender systems were made.