6 projects found that use the agpl license.


Logo ELKI 0.7.0

by erich - November 27, 2015, 18:23:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17333 views, 3182 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.

Changes:

Additions and Improvements from ELKI 0.6.0:

ELKI is now available on Maven: https://search.maven.org/#artifactdetails|de.lmu.ifi.dbs.elki|elki|0.7.0|jar

Please clone https://github.com/elki-project/example-elki-project 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.

K-means:

  • 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.

X-means.

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.

Preprocessing:

  • 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).

Mathematics:

  • 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 Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 4687 views, 1166 downloads, 2 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Changes:

Initial Announcement on mloss.org.


Logo RiVal 0.1

by alansaid - July 29, 2015, 12:39:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1102 views, 283 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.

Changes:

Initial Announcement on mloss.org.


Logo PredictionIO 0.7.0

by simonc - April 29, 2014, 20:59:57 CET [ Project Homepage BibTeX Download ] 9995 views, 2088 downloads, 2 subscriptions

About: Open Source Machine Learning Server

Changes:
  • Single machine version for small-to-medium scale deployments
  • Integrated GraphChi (disk-based large-scale graph computation) and algorithms: ALS, CCD++, SGD, CLiMF
  • Improved runtime for training and offline evaluation
  • Bug fixes

See release notes - https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801


Logo MLWizard 5.2

by remat - July 26, 2012, 15:04:14 CET [ Project Homepage BibTeX Download ] 4359 views, 1067 downloads, 1 subscription

About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well.

Changes:

Faster parameter optimization using genetic algorithm with predefined start population.


Logo JMLR Surrogate Modeling Toolbox 7.0.2

by dgorissen - September 4, 2010, 07:48:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15271 views, 4288 downloads, 1 subscription

About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive.

Changes:

Incremental update, fixing some cosmetic issues, coincides with JMLR publication.