Computational Statistics & Data Analysis
Implementation and integration of algorithms into the KEEL data-mining software tool
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Jstacs: a java framework for statistical analysis and classification of biological sequences
The Journal of Machine Learning Research
Context-aware prediction of user's first click
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
The transformation of surgery patient care with a clinical research information system
Expert Systems with Applications: An International Journal
Bob: a free signal processing and machine learning toolbox for researchers
Proceedings of the 20th ACM international conference on Multimedia
ACSC '12 Proceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 122
Stratified sampling of execution traces: Execution phases serving as strata
Science of Computer Programming
Automated content labeling using context in email
Proceedings of the 17th International Conference on Management of Data
Mining sequential patterns with extensible knowledge representation
Intelligent Data Analysis
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Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists. The interfaces for each type of algorithm are kept simple and algorithms strictly follow their respective interface. Comparing different classifiers or clustering algorithms is therefore straightforward, and implementing new algorithms is also easy. The implementations of the algorithms are clearly written, properly documented and can thus be used as a reference. The library is written in Java and is available from http://java-ml.sourceforge.net/ under the GNU GPL license.