Projects supporting the csv data format.
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Logo ELKI 0.7.0

by erich - November 27, 2015, 18:23:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15731 views, 2865 downloads, 4 subscriptions

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.


Additions and Improvements from ELKI 0.6.0:

ELKI is now available on Maven:|de.lmu.ifi.dbs.elki|elki|0.7.0|jar

Please clone for a minimal project example.

Uncertain data types, and clustering algorithms for uncertain data.

Major refactoring of distances - removal of Distance values and removed support for non-double-valued distance functions (in particular DoubleDistance was removed). While this reduces the generality of ELKI, we could remove about 2.5% of the codebase by not having to have optimized codepaths for double-distance anymore. Generics for distances were present in almost any distance-based algorithm, and we were also happy to reduce the use of generics this way. Support for non-double-valued distances can trivially be added again, e.g. by adding the specialization one level higher: at the query instead of the distance level, for example. In this process, we also removed the Generics from NumberVector. The object-based get was deprecated for a good reason long ago, and e.g. doubleValue are more efficient (even for non-DoubleVectors).

Dropped some long-deprecated classes.


  • speedups for some initialization heuristics.

  • K-means++ initialization no longer squares distances (again).

  • farthest-point heuristics now uses minimum instead of sum (renamed).

  • additional evaluation criteria.

  • Elkan's and Hamerly's faster k-means variants.

CLARA clustering.


Hierarchical clustering:

  • Renamed naive algorithm to AGNES.

  • Anderbergs algorithm (faster than AGNES, slower than SLINK).

  • CLINK for complete linkage clustering in O(n²) time, O(n) memory.

  • Simple extraction from HDBSCAN.

  • "Optimal" extraction from HDBSCAN.

  • HDBSCAN, in two variants.

LSDBC clustering.

EM clustering was refactored and moved into its own package. The new version is much more extensible.

OPTICS clustering:

  • Added a list-based variant of OPTICS to our heap-based.

  • FastOPTICS (contributed by Johannes Schneider).

  • Improved OPTICS Xi cluster extraction.

Outlier detection:

  • KDEOS outlier detection (SDM14).

  • k-means based outlier detection (distance to centroid) and Silhouette coefficient based approach (which does not work too well on the toy data sets - the lowest silhouette are usually where two clusters touch).

  • bug fix in kNN weight, when distances are tied and kNN yields more than k results.

  • kNN and kNN weight outlier have their k parameter changed: old 2NN outlier is now 1NN outlier, as commonly understood in classification literature (1 nearest neighbor other than the query object; whereas in database literature the 1NN is usually the query object itself). You can get the old result back by decreasing k by one easily.

  • LOCI implementation is now only O(n^3 log n) instead of O(n^4).

  • Local Isolation Coefficient (LIC).

  • IDOS outlier detection with intrinsic dimensionality.

  • Baseline intrinsic dimensionality outlier detection.

  • Variance-of-Volumes outlier detection (VOV).

Parallel computation framework, and some parallelized algorithms

  • Parallel k-means.

  • Parallel LOF and variants.

LibSVM format parser.

kNN classification (with index acceleration).

Internal cluster evaluation:

  • Silhouette index.

  • Simplified Silhouette index (faster).

  • Davis-Bouldin index.

  • PBM index.

  • Variance-Ratio-Criteria.

  • Sum of squared errors.

  • C-Index.

  • Concordant pair indexes (Gamma, Tau).

  • Different noise handling strategies for internal indexes.

Statistical dependence measures:

  • Distance correlation dCor.

  • Hoeffings D.

  • Some divergence / mutual information measures.

Distance functions:

  • Big refactoring.

  • Time series distances refactored, allow variable length series now.

  • Hellinger distance and kernel function.


  • Faster MDS implementation using power iterations.

Indexing improvements:

  • Precomputed distance matrix "index".

  • iDistance index (static only).

  • Inverted-list index for sparse data and cosine/arccosine distance.

  • Cover tree index (static only).

  • Additional LSH hash functions.

Frequent Itemset Mining:

  • Improved APRIORI implementation.

  • FP-Growth added.

  • Eclat (basic version only) added.

Uncertain clustering:

  • Discrete and continuous data models.

  • FDBSCAN clustering.

  • UKMeans clustering.

  • CKMeans clustering.

  • Representative Uncertain Clustering (Meta-algorithm).

  • Center-of-mass meta Clustering (allows using other clustering algorithms on uncertain objects).


  • Several estimators for intrinsic dimensionality.

MiniGUI has two "secret" new options: -minigui.last -minigui.autorun to load the last saved configuration and run it, for convenience.

Logging API has been extended, to make logging more convenient in a number of places (saving some lines for progress logging and timing).

Logo KeLP 2.0.0

by kelpadmin - November 26, 2015, 16:14:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4000 views, 994 downloads, 3 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code.


This is a major release that includes brand new features as well as a renewed architecture of the entire project.

Now KeLP is organized in four maven projects:

  • kelp-core: it contains the infrastructure of abstract classes and interfaces to work with KeLP. Furthermore, some implementations of algorithms, kernels and representations are included, to provide a base operative environment.

  • kelp-additional-kernels: it contains several kernel functions that extend the set of kernels made available in the kelp-core project. Moreover, this project implements the specific representations required to enable the application of such kernels. In this project the following kernel functions are considered: Sequence kernels, Tree kernels and Graphs kernels.

  • kelp-additional-algorithms: it contains several learning algorithms extending the set of algorithms provided in the kelp-core project, e.g. the C-Support Vector Machine or ν-Support Vector Machine learning algorithms. In particular, advanced learning algorithms for classification and regression can be found in this package. The algorithms are grouped in: 1) Batch Learning, where the complete training dataset is supposed to be entirely available during the learning phase; 2) Online Learning, where individual examples are exploited one at a time to incrementally acquire the model.

  • kelp-full: this is the complete package of KeLP. It aggregates the previous modules in one jar. It contains also a set of fully functioning examples showing how to implement a learning system with KeLP. Batch learning algorithm as well as Online Learning algorithms usage is shown here. Different examples cover the usage of standard kernel, Tree Kernels and Sequence Kernel, with caching mechanisms.

Furthermore this new release includes:

  • CsvDatasetReader: it allows to read files in CSV format

  • DCDLearningAlgorithm: it is the implementation of the Dual Coordinate Descent learning algorithm

  • methods for checking the consistency of a dataset.

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.0!

Logo PROFET 1.0.0

by Hamda - November 26, 2015, 13:20:28 CET [ Project Homepage BibTeX Download ] 140 views, 27 downloads, 1 subscription

About: Software for Automatic Construction and Inference of DBNs Based on Mathematical Models


Initial Announcement on

Logo bandicoot 0.4

by yvesalexandre - November 20, 2015, 17:08:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 298 views, 55 downloads, 2 subscriptions

About: An open-source Python toolbox to analyze mobile phone metadata.


Initial Announcement on

Logo ADAMS 0.4.11

by fracpete - November 18, 2015, 10:58:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15418 views, 3086 downloads, 3 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.


Some highlights of this release:

  • switch to Java 8
  • preferred IDE is now IntelliJ IDEA
  • removed OSX builds
  • 43 new actors
  • 13 new conversions
  • removed obsolete actors and conversions
  • added video support (video files and webcams)
  • added object detection and tracking (incl recording of object trails)
  • proof-of-concept remote-execution of jobs
  • SSH console
  • support for webscraping using JSoup
  • MEKA upgraded to 1.9.0
  • MOA regressor support added
  • better syntax highlighting for Groovy/Jython
  • several new Weka classifiers (eg Veto, LeanMultiScheme, ThresholdedBinaryClassification, InputSmearing)
  • new genetic algorithm: Hermione
  • extended the abstaining classifier framework (integrates with Weka)
  • adams-imaging split into: adams-imaging, adams-boofcv, adams-imagemagick, adams-imagej, adams-openimaj (newly added)

Logo Armadillo library 6.200

by cu24gjf - November 15, 2015, 06:54:50 CET [ Project Homepage BibTeX Download ] 68728 views, 14004 downloads, 5 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

  • expanded diagmat() to handle non-square matrices and arbitrary diagonals
  • expanded trace() to handle non-square matrices
  • correction for datum::Z_0 constant
  • bug fixes for sparse eigen decomposition

Logo Probabilistic Classification Vector Machine 0.22

by fmschleif - November 10, 2015, 13:16:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2980 views, 674 downloads, 3 subscriptions

About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine.


30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released

Logo BayesPy 0.4.1

by jluttine - November 2, 2015, 13:40:09 CET [ Project Homepage BibTeX Download ] 9861 views, 2337 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

  • Define extra dependencies needed to build the documentation

Logo Cognitive Foundry 3.4.2

by Baz - October 30, 2015, 06:53:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23293 views, 3909 downloads, 4 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

  • General:
    • Upgraded MTJ to 1.0.3.
  • Common:
    • Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
    • Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
    • Optimized DenseVector by removing a layer of indirection.
    • Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
    • Added utility class for enumerating combinations.
    • Adjusted ScalarMap implementation hierarchy.
    • Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
    • Added method for creating square identity matrix to MatrixFactory.
    • Added Random implementation that uses a cached set of values.
  • Learning:
    • Implemented feature hashing.
    • Added factory for random forests.
    • Implemented uniform distribution over integer values.
    • Added Chi-squared similarity.
    • Added KL divergence.
    • Added general conditional probability distribution.
    • Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
    • Fixed null pointer exception that can happen in K-means with an empty cluster.
    • Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
  • Text:
    • Improvements to LDA Gibbs sampler.

Logo JMLR dlib ml 18.18

by davis685 - October 29, 2015, 01:48:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 120262 views, 20011 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.


This release has focused on build system improvements, both for the Python API and C++ builds using CMake. This includes adding a script for installing the dlib Python API as well as a make install target for installing a C++ shared library for non-Python use.

Logo MLweb 0.1.2

by lauerfab - October 9, 2015, 11:55:52 CET [ Project Homepage BibTeX Download ] 1464 views, 402 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

  • Add Regression:AutoReg method
  • Add KernelRidgeRegression tuning function
  • More efficient predictions for KRR, SVM, SVR
  • Add BFGS optimization method
  • Faster QR, SVD and eigendecomposition
  • Better support for sparse vectors and matrices
  • Add linear algebra benchmark at
  • Fix plots in LALOlib/ML.js
  • Fix cross-origin issues in new MLlab()
  • Small bug fixes

Logo python weka wrapper 0.3.3

by fracpete - September 26, 2015, 06:11:42 CET [ Project Homepage BibTeX Download ] 19339 views, 4124 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

  • updated to Weka 3.7.13
  • documentation now covers the API as well

Logo YCML 0.2.2

by yconst - August 24, 2015, 20:28:45 CET [ Project Homepage BibTeX Download ] 808 views, 152 downloads, 3 subscriptions

About: A Machine Learning framework for Objective-C and Swift (OS X / iOS)


Initial Announcement on

Logo Java Data Mining Package 0.3.0

by arndt - August 19, 2015, 15:44:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1136 views, 208 downloads, 3 subscriptions

About: A Java library for machine learning and data analytics


Initial Announcement on

Logo RiVal 0.1

by alansaid - July 29, 2015, 12:39:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 829 views, 207 downloads, 2 subscriptions

About: Rival is an open source Java toolkit for recommender system evaluation. It provides a simple way to create evaluation results comparable across different recommendation frameworks.


Initial Announcement on

Logo NaN toolbox 2.8.1

by schloegl - July 6, 2015, 22:43:23 CET [ Project Homepage BibTeX Download ] 39350 views, 8206 downloads, 3 subscriptions

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.


Changes in v.2.8.1 - number of bug fixes - compatibility issues with recent versions of Octave are addressed - upgrade to libsvm 3-12

For details see the CHANGELOG at

About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems.


Initial Announcement on

Logo deepdetect 0.1

by beniz - June 2, 2015, 09:25:28 CET [ Project Homepage BibTeX Download ] 1006 views, 284 downloads, 3 subscriptions

About: A Deep Learning API and server


Initial Announcement on

Logo streamDM 0.0.1

by abifet - April 28, 2015, 12:34:00 CET [ Project Homepage BibTeX Download ] 1103 views, 453 downloads, 1 subscription

About: streamDM is a new open source data mining and machine learning library, designed on top of Spark Streaming, an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of data streams.


Initial Announcement on

Logo Choquistic Utilitaristic Regression 1.00

by AliFall - April 17, 2015, 11:31:20 CET [ BibTeX BibTeX for corresponding Paper Download ] 985 views, 402 downloads, 2 subscriptions

About: This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization, see [1].


Initial Announcement on

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