Java Data Mining 2.0 – Early Draft Review

An early draft of Java Data Mining 2.0 (JSR-000247) is available.

In JDM 2.0, data mining includes the functional areas of classification, regression, attribute importance, clustering, association, feature extraction, time series, and anomaly detection. These are supported by such supervised and unsupervised learning algorithms as decision trees, neural networks, Naive Bayes, Support Vector Machine, K-Means, Apriori, Non-negative Matrix Factorization, and ARIMA.

JDM supports common data mining operations such as model build, test, and apply (score). JDM also supports the creation, persistence, access, and maintenance of metadata supporting mining activities.

Also in JDM 2.0, the standard includes extensions for basic text mining, statistics, and transformations integrated with the mining process. A particular implementation of this specification may not necessarily support all interfaces and services defined by JDM. However, JDM provides a mechanism for discovery of supported interfaces and capabilities.

Published by

Daniel Lemire

A computer science professor at the University of Quebec (TELUQ).

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