Support vector machine  Wikipedia, the free encyclopedia   Support vector machine : In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a nonprobabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.  03Sep2014 
 Frequent Pattern Mining  Apriori Algorithm Heres a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern Un... 22Aug2014  Market Basket Analysis with Mahout  DATASCIENCE HACKS   Also known as Affinity Analysis/Frequent Pattern Mining: Finding patterns in huge amounts of customer transactional data is called market basket analysis. This is useful where store's transactional data is readily available. Using market basket analysis, one can find purchasing patterns. Market basket analysis is also called associative rule mining (actually its otherway around) or affinity?  22Aug2014 
