Frequent Itemset Mining Implementations Repository. Real-time Discovery Engine - YourVersion: Discover Your Version of the Web™ Real-time Discovery Engine - YourVersion: Discover Your Version of the Web™ Statistical Data Mining Tutorials. The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning. I hope they're useful (and please let me know if they are, or if you have suggestions or error-corrections). Click here for a short list of topics. Real-time Discovery Engine - YourVersion: Discover Your Version of the Web™
Real-time Discovery Engine - YourVersion: Discover Your Version of the Web™ Data mining - Overwhelmed by Machine Learning. Data Mining. In an era where computers are widespread in society, people are collecting all sorts of data, mostly in an attempt to enhance their understanding and insights into various processes.
For example, companies and organisations are collecting data concerning the preferences and behaviours of their customers and clients, and use data-mining techniques to extract useful knowledge from these data. Data Mining has close relationship to Statistics, Machine Learning, and Artificial Intelligence, and involves research into learning representations from data, the mathematics of learning, the process of data mining and knowledge discovery, and the construction and exploitation of software tools.