Data Mining (P2)

The analysis of large datasets to uncover previously unknown patterns, trends or relationships

• Helps to understand behaviors and hidden trends/patterns • Helps in detecting risk and fraud • Helps analyze large amounts of data very quickly

• Can reveal insights that compromise anonymity and privacy • Expensive • Requires a large amount of data

It uses unsupervised learning. It divides the data into groups (“clusters”) that have similar characteristics without making any assumptions about what the different clusters means.

Classification is a supervised learning task where the goal is to assign labels or categories to new data based on patterns learned from training data.

It uses unsupervised learning. Breaks up the data sets into different variables (e.g. gender, age, etc.) and tries to find relationships and dependencies between different variables.

It uses unsupervised learning. Essentially, it establishes relationships/associations between different entities within a data set. It starts off by deciding what is the criteria to create a link between entities, then sees what entities meet the criteria, which do not, and to what extent. Often used in a criminal situation.

It uses unsupervised learning. This data mining technique’s purpose is to detect the anomalies and outliers in a dataset.