1.4 What kind of patterns can be mined

Data warehousing
- multidimensional data model
- Data cube technology
- OLAP (online analytical processing)
Multidimensional class or concept description: Characterization and discrimination
Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet region
Data characterised by a user specified class description can be retrieved from a warehouse
Data discrimination can compare features of selected classes
OLAP operations can be used to summarise data along user-specified dimensions

Frequent patterns (or frequent itemsets)
- What prescribed drugs are frequently taken together? What welfare payments are frequently received together?
Association, correlation vs. causality
- A typical association rule: Tertiary Education -> Atheist [10%, 20%] (support, confidence)
- Are strongly associated items also strongly correlated?
How to mine such patterns and rules efficiently in large datasets?
How to use such patterns for classification, clustering, and other applications?

Classification
- Construct models (functions) based on some training examples (supervised learning)
- Describe and distinguish classes or concepts for future prediction
- Predict unknown discrete class labels
- e.g., classify countries based on climate, or classify cars based on fuel efficiency
Regression (also called numerical prediction)
Construct models based on some training examples (supervised learning)
Predict unknown continuous values
e.g. predict weight from height and age, or predict precipitation based on geo-location and cloud patterns.
Typical methods - Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, …
Typical applications - Credit card or taxation fraud detection, direct marketing, classifying stars, diseases, web-pages, …