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Introduction to Java Data Mining (JDM) API and Its Functionalities

This document provides an overview of the Java Data Mining (JDM) API, covering its essential functional areas, including classification, regression, clustering, and association. It highlights the algorithms used in JDM, such as decision trees, neural networks, and K-Means, and the importance of leveraging emerging data mining standards. Additionally, it outlines the benefits of a pure Java API for developing data mining-enabled applications, focusing on operations, metadata management, and the standardization of data mining functionalities across different platforms.

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Introduction to Java Data Mining (JDM) API and Its Functionalities

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  1. JSR 73: Data Mining API 資工三 B90902008 林宗澤

  2. Introduction • In JDM, data mining [Mitchell1997, BL1997] includes the functional areas of classification, regression, attribute importance1, clustering, and association. • These are supported by such supervised and unsupervised learning algorithms as decision trees, neural networks, Naive Bayes, Support Vector Machine, K-Means, and Apriori, on structured data.

  3. JDM is based on a generalized, object-oriented, data mining conceptual model leveraging emerging data mining standards such the Object Management Group’s Common Warehouse Metadata (CWM), ISO’s SQL/MM for Data Mining, and the Data Mining Group’s Predictive Model Markup Language (PMML), as appropriate

  4. Benefits • The Java Data Mining (JDM) specification addresses the need for a pure Java API to facilitate development of data mining-enabled applications. JDM supports common data mining operations, as well as the creation, persistence, access, and maintenance of metadata supporting mining activities. • The ability to leverage data mining functionality via a standard API

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