Oracle Advanced Analytics SQL API Data Mining Algorithms

Oracle Advanced Analytic's Oracle Data Mining (ODM) component provides a broad range of in-database implementations of powerful workhorse data mining techniques and algorithms to solve many types of business problems:

TechniqueApplicabilityAlgorithms
Classification
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Most commonly used technique for predicting a specific outcome such as response / no-response, high / medium / low-value customer, likely to buy / not buy.Logistic Regression —classic statistical technique but now available inside the Oracle Database and supports text and transactional data

Naive Bayes —Fast, simple, commonly applicable

Support Vector Machine—Next generation, supports text and wide data

Decision Tree —Popular, provides human-readable rules
 

Regression
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Technique for predicting a continuous numerical outcome such as customer lifetime value, house value, process yield rates.

Multiple Regression —classic statistical technique but now available inside the Oracle Database and supports text and transactional data

Support Vector Machine —Next generation, supports text and wide data

Attribute Importance
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Ranks attributes according to strength of relationship with target attribute. Use cases include finding factors most associated with customers who respond to an offer, factors most associated with healthy patients.Minimum Description Length—Considers each attribute as a simple predictive model of the target class
Anomaly Detection
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Identifies unusual or suspicious cases based on deviation from the norm. Common examples include health care fraud, expense report fraud, and tax compliance.One-Class Support Vector Machine —Trains on "normal" cases to flag unusual cases
Clustering
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Useful for exploring data and finding natural groupings. Members of a cluster are more like each other than they are like members of a different cluster. Common examples include finding new customer segments, and life sciences discovery.Enhanced K-Means—Supports text mining, hierarchical clustering, distance based

Orthogonal Partitioning Clustering—Hierarchical clustering, density based

Expectation MaximizationClustering technique that performs well in mixed data (dense and sparse) data mining problems.
Association
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Finds rules associated with frequently co-occuring items, used for market basket analysis, cross-sell, root cause analysis. Useful for product bundling, in-store placement, and defect analysis.Apriori—Industry standard for market basket analysis
Feature Selection and Extraction
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Produces new attributes as linear combination of existing attributes. Applicable for text data, latent semantic analysis, data compression, data decomposition and projection, and pattern recognition.Non-negative Matrix Factorization—Next generation, maps the original data into the new set of attributes

Principal Components Analysis (PCA)—creates new fewer composite attributes that respresent all the attributes.

Singular Vector Decomposition—established feature extraction method that has a wide range of applications.
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