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  • Hub Miner: a hubness-aware machine learning experimentation library.
  • Copyright (C) 2014 Nenad Tomasev. Email: nenad.tomasev at
  • This program is free software: you can redistribute it and/or modify it under
  • the terms of the GNU General Public License as published by the Free Software
  • Foundation, either version 3 of the License, or (at your option) any later
  • version.
  • This program is distributed in the hope that it will be useful, but WITHOUT
  • ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
  • FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
  • You should have received a copy of the GNU General Public License along with
  • this program. If not, see */

Welcome to Hub Miner!

Hub Miner logo

It is a machine learning library aimed specifically at overcoming issues in high-dimensional data analysis and is focused mostly at the phenomenon of hubness, which is the asymmetric distribution of relevance within the models. It is a detrimental aspect of the well known curse of dimensionality, so hubness-aware methods (implicit or explicit) are necessary for effective instance-based learning in many dimensions. Hub Miner implements custom methods for classification, clustering, metric learning, instance selection and other common machine learning tasks, as well as a set of baselines and a powerful experimental framework that includes testing under various challenging conditions. Common data file formats are supported, including ARFF, csv and tsv. A full manual is in preparation, but the code is very well documented, so You are encouraged to have a look at some of the source files and online resources at and contact the author with any questions at this stage. The code is tested, stable and reliable - or so it should be - so feel free to notify of any particular issues if they arise.

This is the first release and updates are already under way, so - expect this library to grow and be even better documented and supported.

As for dependencies, these are is the current list:

apiconnector-fat.jar collections-generic-4.01.jar colt-1.2.0.jar commons-codec-1.3.jar commons-httpclient-3.0.1.jar commons-logging-1.1.jar concurrent-1.3.4.jar gson-2.3.jar guice-3.0.jar iText-2.1.7_mx-1.0.jar Jama-1.0.2.jar jcommon-1.0.17.jar jdom.jar jetty-6.1.1.jar jetty-util-6.1.1.jar jfreechart-1.0.14.jar jgraph.jar jgraphx.jar json.jar jsoup-1.7.2.jar jtidy-r7.jar jung-algorithms-2.0-beta1.jar jung-api-2.0-beta1.jar jung-graph-impl-2.0-beta1.jar jung-jai-samples-2.0-beta1.jar jung-visualization-2.0-beta1.jar junit-4.7.jar mdsj.jar mxgraph-all.jar rome-0.8.jar servlet-api-2.5-6.1.1.jar servlet.jar swing-layout-1.0.3.jar swingx-1.6.jar swingx-beaninfo-1.6.jar swingx-ws-1.0.jar TGGraphLayout.jar xercesImpl.jar xmlunit1.0.jar

A dependency on OpenML is apiconnector-fat.jar and it can be downloaded from

All Hub Miner code is in Java and should be portable.

A small part of library that has to do with SIFT feature analysis still relies on having the SiftWin binary in the path and ImageMagick. However, this is just a few methods and unless You plan to use Hub Miner for image feature extraction (which is not its main purpose) - You should be fine without it. I intend to remove this dependency in future builds and switch over to some Java-based image feature extraction libraries, as well as provide better support for OpenCV formats.


Hubness-aware machine learning library.







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