Weighted cluster ensemble based on partition relevance analysis with reduction step

Nejc Ilc (Author)

Abstract

Over the last decade, the advent of the cluster ensemble framework has enabled more accurate and robust data analysis than traditional single clustering algorithms. The improved clustering of microarray data has had a particularly strong impact in the fields of genomics and medicine. However, when we bring several ensemble members together to form a consensus, low-quality data partitions can seriously compromise the final solution. One way to overcome this problem is the weighted cluster ensemble approach based on Partition Relevance Analysis (PRA), which uses internal cluster validity indices to evaluate and weight the ensemble members before the fusion. Unfortunately, the selection of appropriate validation indices for given data is far from trivial. In this paper, we propose an additional step in PRA that reduces the size of the committee of cluster validation indices. It does so by eliminating redundant and noisy indices using data dimensionality reduction methods. Our extension works in an unsupervised way, minimizing the amount of user intervention and required expert knowledge. We adapted three conventional consensus functions based on the principle of evidence accumulation to work with PRA weights. We demonstrate the advantages of the proposed reduction step of PRA based on extensive experiments with 25 gene expression and 15 non-genetic real-world datasets, where we compared 15 consensus functions. The source code is available at https://github.com/nejci/PRAr.

Keywords

analiza gruč;kazalci kvalitete razvrstitve;zmanševanje razsežnosti podatkov;učenje z ansamblom;izločanje značilnic;izbira značilnic;genska izraženost;utežen ansambel;cluster analysis;cluster validity index;dimensionality reduction;ensemble learning;feature extraction;feature selection;gene expression;weighted ensemble;

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004
COBISS:	20600067
ISSN:	2169-3536
Views:	387
Downloads:	130
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	analiza gruč;kazalci kvalitete razvrstitve;zmanševanje razsežnosti podatkov;učenje z ansamblom;izločanje značilnic;izbira značilnic;genska izraženost;utežen ansambel;
Type (COBISS):	Article
Pages:	str. 113720-113736
Issue:	ǂVol. ǂ8
Chronology:	Jun. 2020
DOI:	10.1109/ACCESS.2020.3003046
ID:	12688986