Several Java libraries that can be used for data mining and statistical analysis

Http://itindex.net/blog/2015/01/09/1420751820000.html

Weka:weka is a collection of machine learning algorithms that can be used for data mining tasks. The algorithm can be applied directly to a dataset or called from its own Java code. Weka contains data preprocessing, classification, regression, clustering, association rules, and visualization tools. It is also ideal for developing new machine learning scenarios.

Jmotif: Time series, classification, data Mining Development Library

Java-ml:java Machine Learning Library, clustering, classification, feature selection,

Flanagan: Mathematical and statistical Java Development Library, including regression algorithm, one time two linear nonlinear regression algorithm, data smoothing algorithm, Fourier transform, numerical integration, interpolation method. http://www.ee.ucl.ac.uk/~mflanaga/java/

Mahout:apache Mahout is an open-source project under the Apache software Foundation (ASF), which provides a number of extensible machine learning domain classic algorithms that are designed to help developers create smart applications more quickly and easily. Classical algorithms include clustering, classification, collaborative filtering, evolutionary programming, and so on, and the support for Apache Hadoop has been added to the latest version of Mahout, enabling these algorithms to run more efficiently in cloud computing environments.

Matlab:

Jmulti: Time series Analysis Development Library

Several Java libraries that can be used for data mining and statistical analysis