1.3.3 Unsupervised Learning
Unsupervised learning is non-directed. There is no distinction between dependent and independent attributes. There is no previously-known result to guide the algorithm in building the model.
Unsupervised learning can be used for descriptive purposes. In unsupervised learning, the goal is pattern detection. It can also be used to make predictions.
Oracle Machine Learning supports the following unsupervised machine learning functions:
Table 1-2 Unsupervised Machine Learning Functions
| Function | Description | Sample Problem | Supported Algorithms |
|---|---|---|---|
| Anomaly Detection | Identifies rows (cases, examples) that do not satisfy the characteristics of "normal" data | Given demographic data about a set of customers, identify which customer purchasing behaviors are unusual in the dataset, which may be indicative of fraud. | |
| Association | Finds items that tend to co-occur in the data and specifies the rules that govern their co-occurrence | Find the items that tend to be purchased together and specify their relationship | Apriori |
| Clustering | Finds natural groupings in the data | Segment demographic data into clusters and rank the probability that an individual belongs to a given cluster | |
| Feature Extraction | Creates new attributes (features) using linear combinations of the original attributes | Given demographic data about a set of customers, transform the original attributes into fewer new attributes. | |
| Row Importance | Row importance technique is used in dimensionality reduction of large data sets. Row importance identifies the most influential rows of the data set. | Given a data set, select rows that meet a minimum importance value prior to model building. | cur Matrix Decomposition |
Parent topic: Machine Learning Techniques and Algorithms