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Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities

Dong Huang, Chang-Dong Wang, Hongxing Peng, Jianhuang Lai, Chee-Keong Kwoh
2018 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Finally, two novel consensus functions are proposed to obtain the consensus clustering result.  ...  To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks.  ...  TWO TYPES OF CONSENSUS FUNCTIONS In this section, we propose two consensus functions to obtain the final consensus clustering in the proposed ensemble clustering by propagating cluster-wise similarities  ... 
doi:10.1109/tsmc.2018.2876202 fatcat:n6yrklkz7regdlvidmmi3bythm

Ensembled Semi Supervised Clustering Approach for High Dimensional Data

M. LeelaReddy
2017 International Journal for Research in Applied Science and Engineering Technology  
The incremental ensemble member selection process is newly designed to remove redundant ensemble members based on a newly proposed local cost function and a global cost function, and the normalized cut  ...  algorithm is adopted to serve as the consensus function for providing more stable, robust, and accurate results.  ...  Next, a consensus matrix is constructed based on the set of clustering solutions.  ... 
doi:10.22214/ijraset.2017.4210 fatcat:7bdg3lsiy5bylgts5jsqbhlwwu

A convergent functional architecture of the insula emerges across imaging modalities

Clare Kelly, Roberto Toro, Adriana Di Martino, Christine L. Cox, Pierre Bellec, F. Xavier Castellanos, Michael P. Milham
2012 NeuroImage  
Clustering of these three different covariance-based measures revealed a convergent elemental organization of the insula that likely reflects a fundamental brain architecture governing both brain structure  ...  We parcellated the insula on the basis of three distinct neuroimaging modalities -taskevoked coactivation, intrinsic (i.e., task-independent) functional connectivity, and grey matter structural covariance  ...  Acknowledgments This work was supported by Grants from the National Institute on Drug Abuse (R03DA024775 to CK, 2T32DA007254-16A2 to CLC and R01DA016979 to FXC), the National Institutes of Mental Health  ... 
doi:10.1016/j.neuroimage.2012.03.021 pmid:22440648 pmcid:PMC3376229 fatcat:bdpuxyhvjrhyxjcmqcen2ckccy

TFBScluster web server for the identification of mammalian composite regulatory elements

I. J. Donaldson, B. Gottgens
2006 Nucleic Acids Research  
whole genome sequences, we have developed the TFBScluster web server that integrates several tools for the genome-wide identification and subsequent characterization of transcription factor binding site clusters  ...  TFBScluster has the ability to retain or exclude cluster candidates based on the presence of user supplied IUPAC consensus sequences.  ...  This dataset is based on a recent study that identified active promoters using a microarray based chromatin immunopreciptation method to detect all RNA polymerase II preinitiation complexes assembled on  ... 
doi:10.1093/nar/gkl041 pmid:16845063 pmcid:PMC1538905 fatcat:qfx4xsjknrcmxd6dofxjzvupiu

Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

Yalamarthi Leela Sandhya Rani, V. Sucharita, K. V. V. Satyanarayana
2018 International Journal of Electrical and Computer Engineering (IJECE)  
This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.</p>  ...  Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data.  ...  The clustering ensemble is introduced based on particle swarm clustering. The particle swarm clustering is act as a base clusterer and as well as consensus function is a challenging element.  ... 
doi:10.11591/ijece.v8i4.pp2351-2357 fatcat:6khonvlpsreg3h6slnio4jki7m

Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond [article]

Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Chee-Keong Kwoh
2021 IEEE Transactions on Cybernetics   accepted
of consensus functions.  ...  Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types  ...  three types of consensus functions to combine the multiple base clusterings into the final consensus clustering result.  ... 
doi:10.1109/tcyb.2021.3049633 pmid:33961570 arXiv:1710.03113v4 fatcat:3xfvq5ufzjhnvouqwfna6qkgui

A consensus based approach to constrained clustering of software requirements

Chuan Duan, Jane Cleland-Huang, Bamshad Mobasher
2008 Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08  
In this paper, we propose a semisupervised clustering framework, based on a combination of consensus-based and constrained clustering techniques, which can effectively handle these challenges.  ...  Specifically, we provide a probabilistic analysis for informative constraint generation based on a co-association matrix, and utilize consensus clustering to combine multiple constrained partitions in  ...  Our observations are therefore based on fixed levels of granularity.  ... 
doi:10.1145/1458082.1458225 dblp:conf/cikm/DuanCM08 fatcat:drcoqendjzdxtiblpwvjgzikle

Cluster Ensemble Selection

Xiaoli Z. Fern, Wei Lin
2008 Statistical analysis and data mining  
We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance.  ...  Based on our observations, we designed three different selection approaches that jointly consider these two factors.  ...  Here we propose to use an internal quality measure based on an objective function introduced by Strehl and Ghosh for designing consensus functions [17] .  ... 
doi:10.1002/sam.10008 fatcat:mxabzh4oinejdggsjmnftsmucq

Cluster Ensemble Selection [chapter]

Xiaoli Z. Fern, Wei Lin
2008 Proceedings of the 2008 SIAM International Conference on Data Mining  
We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance.  ...  Based on our observations, we designed three different selection approaches that jointly consider these two factors.  ...  Here we propose to use an internal quality measure based on an objective function introduced by Strehl and Ghosh for designing consensus functions [17] .  ... 
doi:10.1137/1.9781611972788.71 dblp:conf/sdm/FernL08 fatcat:3jtuyu67pjdk7ehucktm4fuq5i

Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale

Hyo M. Lee, Ravnoor S. Gill, Fatemeh Fadaie, Kyoo H. Cho, Marie C. Guiot, Seok-Jun Hong, Neda Bernasconi, Andrea Bernasconi
2020 NeuroImage: Clinical  
We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD  ...  Class membership was replicated in two independent datasets.  ...  Their consensus volume label (the union of the two segmentations) was intersected with cortical surfaces to generate surface-based FCD label, which served as input to the clustering algorithm.  ... 
doi:10.1016/j.nicl.2020.102438 pmid:32987299 pmcid:PMC7520429 fatcat:cqz4mu6qdrbrjkmkkhlvyhkk7y

CLU: A new algorithm for EST clustering

Andrey Ptitsyn, Winston Hide
2005 BMC Bioinformatics  
The program is based on the original CLU match detection algorithm, which has improved performance over the widely used d2_cluster.  ...  The continuous flow of EST data remains one of the richest sources for discoveries in modern biology.  ...  The current implementation doesn't keep the alignments of clusters for analysis and generates a cluster consensus based on unsorted pair-wise alignments only.  ... 
doi:10.1186/1471-2105-6-s2-s3 pmid:16026600 pmcid:PMC1637039 fatcat:5jgsis5sofeelkjkh2bxzfhkri

A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining

S. Sarumathi, N. Shanthi, M. Sharmila
2013 International Journal of Computer Applications  
Moreover a myriad of algorithms and methods has been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters.  ...  Consequently this theoretical and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand  ...  This Meta level approach involves these two major tasks of generating a cluster ensemble and then producing a final partition normally referred as the consensus function [15] [13] .  ... 
doi:10.5120/14004-2050 fatcat:6m2ztxxf7jfapnlkmt5hbrlbyi

A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering

S Pavan Kumar Reddy, U Sesadri
unfortunately generate a final data partition based on incomplete information.  ...  Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster  ...  This Meta level methodology involves two major tasks of: 1) generating a cluster ensemble, and 2) producing the final partition, normally referred to as a consensus function.  ... 
doi:10.24297/ijct.v10i8.1468 fatcat:vp5domcoanfhpflqkxayvdjkv4

Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens

Xiaoluan Xia, Lingzhong Fan, Chen Cheng, Simon B. Eickhoff, Junjie Chen, Haifang Li, Tianzi Jiang
2017 Human Brain Mapping  
The consensus clusters from this optimal solution, which was based on the three schemes, were used as the final parcels for the subsequent connection analyses.  ...  Here, we used three complementary parcellation schemes based on tractography, task-independent functional connectivity, and task-dependent co-activation to investigate the regional differentiation within  ...  Clustering metrics and the consensus clusters.  ... 
doi:10.1002/hbm.23636 pmid:28548226 pmcid:PMC5685173 fatcat:iubsixdpk5actjy4p75czmiyhi

Connectivity-Driven Brain Parcellation via Consensus Clustering [chapter]

Anvar Kurmukov, Ayagoz Musabaeva, Yulia Denisova, Daniel Moyer, Boris Gutman
2018 Lecture Notes in Computer Science  
The search for consensus minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations.  ...  We present two related methods for deriving connectivitybased brain atlases from individual connectomes.  ...  We compare resulting parcellations: Conclusion We have presented an approach for generating unified connectivity-based human brain atlases bases on consensus clustering.  ... 
doi:10.1007/978-3-030-00755-3_13 fatcat:nrpoutguknbtvoe5nkbt7zirge
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