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A data mining function is a class of problems that can be solved using data mining. Each function requires one or more algorithms to solve the class of problems.
Classification, regression, and attribute importance are supervised mining functions; associations, feature extraction, clustering, and anomaly detection are unsupervised.
Oracle Data Miner supports the following data mining functions:
Classification: Grouping items into discrete classes and predicting which class an item belongs to; classification algorithms are Decision Tree, Naive Bayes, Generalized Linear Models (Binary Logistic Regression), and Support Vector Machine.
Regression: Approximating and predicting continuous numerical values; the algorithms for regression are Support Vector Machine and Generalized Linear Models (Multivariate Linear Regression).
Anomaly Detection: Detecting anomalous cases, such as fraud and intrusions; the algorithm for anomaly detection is one-class Support Vector Machine.
Attribute Importance: Identifying the attributes that have the strongest relationships with the target attribute (for example, customers likely to churn); the algorithm for attribute importance is Minimum Descriptor Length.
Clustering: Finding natural groupings in the data that are often used for identifying customer segments; the algorithms for clustering are k-Means and O-Cluster.
Associations: Analyzing "market baskets," items that are likely to be purchased together; the algorithm for associations is Apriori.
Feature Extraction: Creating new attributes (features) as a combination of the original attributes; the algorithm for feature extraction is Non-Negative Matrix Factorization.
In addition to mining structured data, ODM permits mining of text data (such as police reports, customer comments, or physician's notes) and spatial data.