* Regression/Estimation
-> Predicting continuous values
Algorithms: Linear Regression, Non-Linear Regression, Multiple Linear Regression
* Classification
-> Predicting the item class/category of a case
Algorithms: K-Nearest Neighbours, Decision Trees, Logistic Regression, Support Vector Machine
* Clustering
-> Finding the structure of data; summarization
Algorithms: k-Means Clustering, Hierarchical Clustering, Density-based Clustering
* Associations
-> Assoicating frequent co-occurring items/events
* Anomaly Detection
-> Discovering abnormal and unusual cases
* Sequence Mining
-> Predicting next events; click-stream (Markov Model, HMM)
* Dimension Reduction
-> Reducing the size of data (PCA)
* Recommendation Systems
-> Recommending items
Algorithms: Content-based Recommendation Engines, Collaborative Filtering
