Consensus clustering: Difference between revisions

Revision as of 03:15, 13 August 2023

Revision as of 01:18, 2 October 2023

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''Punera'' and ''Ghosh'' extended the idea of hard clustering ensembles to the soft clustering scenario. Each instance in a soft ensemble is represented by a concatenation of ''r'' posterior membership probability distributions obtained from the constituent clustering algorithms. We can define a distance measure between two instances using the [[Kullback–Leibler divergence|Kullback–Leibler (KL) divergence]], which calculates the "distance" between two probability distributions.<ref>Kunal Punera, Joydeep Ghosh. [https://web.archive.org/web/20081201150950/http://www.ideal.ece.utexas.edu/papers/2007/punera07softconsensus.pdf Consensus Based Ensembles of Soft Clusterings]</ref>

#'''{{Proper name|sCSPA}}''': extends CSPA by calculating a similarity matrix. Each object is visualized as a point in dimensional space, with each dimension corresponding to probability of its belonging to a cluster. This technique first transforms the objects into a label-space and then interprets the [[dot product]] between the vectors representing the objects as their similarity.

#'''{{Proper name|sMCLA}}''':extends MCLA by accepting soft clusterings as input. sMCLA's working can be divided into the following steps:

#* Construct Soft Meta-Graph of Clusters

#* Group the Clusters into Meta-Clusters

#* Collapse Meta-Clusters using Weighting

#* Compete for Objects

#'''{{Proper name|sHBGF}}''':represents the ensemble as a [[bipartite graph]] with clusters and instances as nodes, and edges between the instances and the clusters they belong to.<ref>Solving cluster ensemble problems by bipartite graph partitioning, Xiaoli Zhang Fern and [[Carla Brodley]], Proceedings of the twenty-first international conference on Machine learning</ref> This approach can be trivially adapted to consider soft ensembles since the graph partitioning algorithm METIS accepts weights on the edges of the graph to be partitioned. In sHBGF, the graph has ''n''&nbsp;+&nbsp;''t'' vertices, where t is the total number of underlying clusters.

#'''Bayesian consensus clustering (BCC)''': defines a fully [[Bayesian probability|Bayesian]] model for soft consensus clustering in which multiple source clusterings, defined by different input data or different probability models, are assumed to adhere loosely to a consensus clustering.<ref name=LockBCC>{{cite journal|last=Lock|first=E.F.|author2=Dunson, D.B. |title=Bayesian consensus clustering|journal=Bioinformatics|date=2013|doi=10.1093/bioinformatics/btt425|pmid=23990412|pmc=3789539|volume=29|number=20|pages=2610–2616|arxiv=1302.7280|bibcode=2013arXiv1302.7280L}}</ref> The full posterior for the separate clusterings, and the consensus clustering, are inferred simultaneously via [[Gibbs sampling]].

#'''Ensemble Clustering Fuzzification Means (ECF-Means)''': ECF-means is a clustering algorithm, which combines different clustering results in ensemble, achieved by different runs of a chosen algorithm ([[k-means]]), into a single final clustering configuration.<ref name=ZazzECF>{{cite journal|last=Zazzaro|first=Gaetano|author2=Martone, Angelo |title=ECF-means - Ensemble Clustering Fuzzification Means. A novel algorithm for clustering aggregation, fuzzification, and optimization |journal=IMM 2018: The Eighth International Conference on Advances in Information Mining and Management|date=2018}} [https://www.thinkmind.org/articles/immm_2018_2_10_50010.pdf]</ref>

== References ==