sebdumancic / ReCeNT

Relational clustering over neighbourhood trees

Relational clustering over neighbourhood trees

A versatile relational clustering toolbox. It contains several clustering algorithms, check the original paper for details (see below). Still under development.

Installation

• get SBT
• clone this repository
• position in the root folder and
  • build a jar with dependencies sbt assembly
  • build a jar without dependencies sbt package

Usage

Arguments

Usage: RelationalClustering.jar [OPTIONS]

OPTIONS

--db filepath                                                       database(s) with data to cluster (May be specified multiple times.)

--declarations file path                                            predicate declarations

--query comma-separated list                                        domains to query

--similarity [RCNT|HS|HSAG|CCFonseca|RKOH]                          similarity measure

--domain filepath                                                   predicate definitions

--aggregates comma-separated list [mean/min/max]                    a list of aggregator functions to use for the numerical attributes

--algorithm [Spectral|Hierarchical|DBscan|Affinity]                 clustering algorithm

--bagCombination [union|intersection]                               multiset combination method

--bagSimilarity [chiSquared|maximum|minimum|union]                  multiset similarity measure

--clauseLength n                                                    (CC and RKOH) maximal length of clause/walk 

--damping d                                                         damping parameter for Affinity Propagation

--definitionsDeviance Double                                        maximum standard deviation for a numeric attribute to be preserved (in % of the mean value)

--definitionsK Int                                                  top K most occurring tuples to select

--depth n                                                           depth of the neighbourhood tree

--eps d                                                             eps value for DBscan

--labels filepath                                                   labels for the query objects (May be specified multiple times.)

--linkage [average|complete|ward]                                   (Hierarchical) linkage

--preference d                                                      (Affinity Propagation) preference parameter

--root filePath                                                     temporary folder to use

--selection [model|saturation]                                      method to choose a single clustering

--selectionValidation [intraCluster|silhouette]                     evaluation criteria for clustering selection

--validationMethod [ARI|RI|intraCluster|majorityClass]              cluster validation method

--vertexCombination [avg|min|max]                                   how to combine the similarities of individual vertices in a hyperedge

--weights Array[Double]                                             
                                                                    comma-separated list of weights [attributes,attribute distribution,connections,vertex neighbourhood,edge distribution]

--exportNTrees flag                                                 export neighbourhood trees as gspan

--findDefinitions flag                                              extract definitions of clusters

-k n                                                                number of clusters to create

--localRepo  flag                                                   use local NodeRepository for all neighbourhood trees

--selectSingle  flag                                                select single clustering

--validate  flag                                                    perform clustering validation

Input files

Knowledge base/graph containing the facts in a domain (*.db)

Movie(Aoceanstwelve,Anelsonpeltz)
Movie(Aplayerthe,Awhoopigoldberg)
Movie(Apelicanbriefthe,Ajuliaroberts)
Movie(Aoceanstwelve,Ajuliaroberts)
...
Gender_male(Adavidsontag)
Gender_male(Arobertculp)
Gender_female(Acynthiastevenson)
Gender_male(Afredward)
Gender_female(Adinamerrill)
...
Genre(Asoderberghsteven,Acrime)
Genre(Apakulaalanj,Adrama)
Genre(Apakulaalanj,Amystery)
Genre(Aaltmanroberti,Adrama)
Workedunder(Aminianden,Asoderberghsteven)
Workedunder(Acaseyaffleck,Asoderberghsteven)
Workedunder(Aelliottgould,Asoderberghsteven)
Workedunder(Adenzelwashington,Apakulaalanj)
...

Definitions file specifying the domains of objects (*.def)

Gender_male(person)
Gender_female(person)
Genre(person,genre)
Movie(movie,person)
Workedunder(person,person)

Declarations file specifying the meaning of the arguments of predicates (*.dcl)

Gender_male(name)
Gender_female(name)
Genre(name,attr)
Movie(name,name)
Workedunder(name,name)

The arguments can have the following roles:

1. name - identifier of an objects/instance/example; this is essentially treated as the name of an instance
2. attr - identifies a discrete attribute value. Attribute name is given by the name of a predicate. It needs to have exactly one name argument.
3. number - identifies a continuous attribute value. Attribute name is given by the name of a predicate. It needs to have exactly one name argument.

These roles influence the way a neighbourhood tree is constructed.

Similarity measures

The following similarity measure a currently supported:

1. Relational clustering over neighbourhood trees (see Citing section)
2. Hybrid similarity measure introduced in Neville, Adler and Jensen: Clustering Relational Data Using Attribute and Link Information. Text Mining and Link Analysis Workshop, ICAI 2003
3. Hybrid similarity measure for annotated graphs introduced in Witsenburg and Blockeel: Improving the accuracy of similarity measures by using link information. Foundations of Intelligent Systems 2001
4. Conceptual clustering introduced in Fonseca, Santos Costa, Camacho: Conceptual clustering of multi-relational data. ILP 2011
5. [Not ready yet] Relational instance based learning
6. Graph kernels:

• Rooted Kernel for ordered hypergraphs from Wachman, Khardon: Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs. ICML 2007

Contributing

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request

Contact

If you have any question, feel free to send then at [e-mail]

Citing

Please cite the following paper if you are using the code

@article{,
    author =       {Dumancic, Sebastijan and Blockeel, Hendrik},
    title =        {An expressive dissimilarity measure for relational clustering over neighbourhood trees},
    journal =      {Machine Learning journal},
    year =         {2017},
    url =          {https://lirias.kuleuven.be/handle/123456789/582293}
}

License

Release under Apache License, version 2.