Oracle Data Mining - (Function|Type of Model)
About
Each data mining function specifies a class of problems that can be modeled and solved. It's not a mathematical function but a categorical function.
| Function | Type | Description |
|---|---|---|
| Attribute Importance | Supervised | Identifies the attributes that are most important in predicting a target attribute |
| Classification | Supervised | Assigns items to discrete classes and predicts the class to which an item belongs |
| Regression | Supervised | Approximates and forecasts continuous values |
| Anomaly Detection | Unsupervised | Identifies items (outliers) that do not satisfy the characteristics of “normal” data |
| Association Rules | Unsupervised | Finds items that tend to co-occur in the data and specifies the rules that govern their co-occurrence |
| Clustering | Unsupervised | Finds natural groupings in the data |
| Feature Extraction | Unsupervised | Creates new attributes (features) using linear combinations of the original attribute |
Data mining functions fall generally into two categories:
- and unsupervised.
Articles Related
Categories
Data mining can be divided into two types of “Learning”, supervised and unsupervised.
Notions of supervised and unsupervised learning are derived from the science of machine learning, which has been called a sub-area of artificial intelligence.
Artificial intelligence refers to the implementation and study of systems that exhibit autonomous intelligence or behavior of their own. Machine learning deals with techniques that enable devices to learn from their own performance and modify their own functioning. Data mining applies machine learning concepts to data.
Oracle Documentation
Supervised
Supervised learning is also known as directed learning.
Supervised Learning has the goal of predicting a value for a particular characteristic, or attribute that describes some behaviour.
| Example | Prediction | Illustrate the Model |
|---|---|---|
| Purchasing Product X | Yes or No | Binary Classification |
| Defaulting on a loan | Yes or No | Binary Classification |
| Failing in the manufacturing process | Yes or No | Binary Classification |
| Producing revenue | Low, Medium, High | Multi-class Classification |
| Selling at a particular price | a specific amount of money | Regression |
| Differing from known cases | Yes or No | One-class Classification |
The attribute being predicted is called the Target Attribute.
Directed data mining attempts to explain the behaviour of the target as a function of a set of independent attributes or predictors.
Supervised learning generally results in predictive models. This is in contrast to unsupervised learning where the goal is pattern detection.
The building of a supervised model involves training.
Supervised mining techniques:
- Scoring (Principal technique)
Target
The target is the (attribute|column) to be predicted
Unsupervised
Unsupervised learning is non-directed.
Unsupervised Learning has the goal of discovering relationships and patterns rather than of determining a particular value. That is, there is no target attribute.
There is:
- no distinction between dependent and independent attributes.
- no previously-known result to guide the algorithm in building the model.
| Example | Illustrate the Model |
|---|---|
| Determine distinct segments of a population and the attribute values indicating an individual’s membership in a particular segment | Clustering |
| Determine the five items most likely to be purchased at the same time as item X. | Market Basket Analysis (or association) |
Unsupervised learning can be used:
- for descriptive purposes.
- to make predictions.
Unsupervised Learning: Oracle Data Mining - Scoring (Applying)
