2024年2月4日 · ELKI（Environment for Developing KDD-Applications Supported by Index-Structures）是一个专门为聚类和离群点检测而设计的数据挖掘平台。 与Weka类似，ELKI也是一个用Java编写的开源平台，提供了丰富的数据挖掘算法和工具。

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.

ELKI is a generic framework for a broad range of KDD-applications and their development. For background, contact-information, and contributors see https://elki-project.github.io/.

Run k-medoids clustering with a given distance query. Not a very elegant API, but needed for some types of nested k-medoids. Get the static class logger.

java.lang.Object elki.visualization.visualizers.AbstractVisualization elki.visualization.visualizers.visunproj.KeyVisualization.Instance All Implemented Interfaces: DataStoreListener, ResultListener, VisualizationListener, Visualization, java.util.EventListener Enclosing class: KeyVisualization

2022年1月22日 · Hi, I would like to use ELKI to validate our OutlierDetection.jl algorithms. It appears that for unsupervised algorithms there is no facility to learn a model from data and predict on another dataset, right?

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ELKI Example Project. Contribute to elki-project/example-elki-project development by creating an account on GitHub.

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