Clustering :four: Ever

Welcome to Clustering:four:Ever, a Big Data Clustering Library gathering clustering, unsupervised algorithms, and quality indices. Don't hesitate to check our Wiki, ask questions or make recommendations in our Gitter.

API documentation

Include it in your project

Add following line in your build.sbt :

• "org.clustering4ever" % "clustering4ever_2.11" % "0.11.0" to your libraryDependencies

Eventually add one of these resolvers :

• resolvers += Resolver.bintrayRepo("clustering4ever", "C4E")
• resolvers += "mvnrepository" at "http://mvnrepository.com/artifact/"

You can also take specifics parts (Core, ScalaClustering, ...) from Bintray or Maven.

Available algorithms

• emphasized algorithms are in Scala.
• bold algorithms are implemented in Spark.
• They can be available in both versions

Clustering algorithms

• Jenks Natural Breaks
• Epsilon Proximity*
  • Scalar Epsilon Proximity*, Binary Epsilon Proximity*, Mixed Epsilon Proximity*, Any Object Epsilon Proximity*
• K-Centers*
  • K-Means*, K-Modes*, K-Prototypes*, Any Object K-Centers*
• Gaussian Mixture
• Self Organizing Maps (Original project)
• G-Stream (Original project)
• PatchWork (Original project)
• Random Local Area *
• OPTICS *
• Clusterwize
• Tensor Biclustering algorithms (Original project)
  • Folding-Spectral, Unfolding-Spectral, Thresholding Sum Of Squared Trajectory Length, Thresholding Individuals Trajectory Length, Recursive Biclustering, Multiple Biclustering
• Ant-Tree *
  • Continuous Ant-Tree, Binary Ant-Tree, Mixed Ant-Tree
• DC-DPM (Original project) - Distributed Clustering based on Dirichlet Process Mixture
• SG2Stream

Algorithm followed with a * can be executed by benchmarking classes.

Preprocessing

• UMAP
• Gradient Ascent (Mean-Shift related)
  • Scalar Gradient Ascent, Binary Gradient Ascent, Mixed Gradient Ascent, Any Object Gradient Ascent
• Rough Set Features Selection

Quality Indices

You can realize manually your quality measures with dedicated class for local or distributed collection. Helpers ClustersIndicesAnalysisLocal and ClustersIndicesAnalysisDistributed allow you to test indices on multiple clustering at once.

• Internal Indices
  • Davies Bouldin
  • Ball Hall
• External Indices
  • Multiple Classification
    • Mutual Information, Normalized Mutual Information
    • Purity
    • Accuracy, Precision, Recall, fBeta, f1, RAND, ARAND, Matthews correlation coefficient, CzekanowskiDice, RogersTanimoto, FolkesMallows, Jaccard, Kulcztnski, McNemar, RusselRao, SokalSneath1, SokalSneath2
  • Binary Classification
    • Accuracy, Precision, Recall, fBeta, f1

Clustering benchmarking and analysis

Using classes ClusteringChainingLocal, BigDataClusteringChaining, DistributedClusteringChaining, and ChainingOneAlgorithm descendants you have the possibility to run multiple clustering algorithms respectively locally and parallel, in a sequentially distributed way, and parallel on a distributed system, locally and parallel, generate much vectorization of the data whilst keeping active information on each clustering including used vectorization, clustering model, clustering number and clustering arguments.

Classes ClustersIndicesAnalysisLocal and ClustersIndicesAnalysisDistributed are devoted for clustering indices analysis.

Classes ClustersAnalysisLocal and ClustersAnalysisDistributed will be used to describe obtained clustering in terms of distributions, proportions of categorical features...

Incoming soon (developped by our team)

• DESOM:Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
• SOM:Kohonen self-organizing map
• SOMperf: SOM performance metrics and quality indices
• skstab is a module for clustering stability analysis in Python with a scikit-learn compatible API
• FunCLBM: Functional Conditional Latent Block Model
• Spark Time Series Set data analysis
• UMAP
• Gaussian Mixture
• DBScan
• Bayesian Optimization for AutoML

Citation

If you publish material based on information obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this community work. This will help others to obtain the same information and replicate your experiments, because having results is cool but being able to compare to others is better. Citation: @misc{C4E, url = “https://github.com/Clustering4Ever/Clustering4Ever“, institution = “Paris 13 University, LIPN UMR CNRS 7030”}

C4E-Notebooks examples

Basic usages of implemented algorithms are exposed with BeakerX and Jupyter notebook through binder :arrow_right: .

They also can be downloaded directly from our Notebooks repository under different format as Jupyter or SparkNotebook.

Miscellaneous

Helper functions to generate Clusterizable collections

You can easily generate your collections with basic Clusterizable using helpers in org.clustering4ever.util.{ArrayAndSeqTowardGVectorImplicit, ScalaCollectionImplicits, SparkImplicits} or explore Clusterizable and EasyClusterizable for more advanced usages.

References

What data structures are recommended for best performances

ArrayBuffer or ParArray as vector containers are recommended for local applications, if data is bigger don't hesitate to pass to RDD.