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Technique

Applicability

Algorithms

Classification



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

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

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

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

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

Association

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 Extraction

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