A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering
2011
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Preserved Fulltext
to improve the stability and accuracy along with a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. ...
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 ...
The proposed link based approach, including the underlying intuition of refining an ensemble-information matrix and details of a link-based. ...
doi:10.24297/ijct.v10i8.1468
fatcat:vp5domcoanfhpflqkxayvdjkv4
A new link-based method to ensemble clustering and cancer microarray data analysis
2014
International Journal of Collaborative Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Among different state-of-the-art methods, the link-based approach (LCE) recently introduced by Iam-On et al. (2011) provides a highly accurate clustering. ...
Ensemble clustering or cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy. ...
In particular, a link-based method (LCE) introduced by Iam- has shown to be more effective than the previous cluster ensemble methods adopted for microarray data analysis. ...
doi:10.1504/ijci.2014.064842
fatcat:5c7eqxgfizcrvabhvdgrvgmx5i
A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining
2013
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
This cluster ensemble is a good alternative approach for facing the cluster analysis problem. ...
The main aspire of the cluster ensemble is to combine different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. ...
Link based Clustering Ensemble Method (LCE) This link based cluster ensemble method denotes the discovery of unknown values in the cluster co-association matrix [25] . ...
doi:10.5120/14004-2050
fatcat:6m2ztxxf7jfapnlkmt5hbrlbyi
A Review article on Semi- Supervised Clustering Framework for High Dimensional Data
2019
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Clustering algorithms are based on active learning, with ensemble clustering-means algorithm, data streams with flock, fuzzy clustering for shape annotations, Incremental semi supervised clustering, Weakly ...
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. ...
A feature selection approach based on an efficient margin based sample weighting algorithm to improve the performance of gene selection investigated how to use model-based entropy to perform feature selection ...
doi:10.32628/cseit195410
fatcat:xl37f2eb6bagjfwdscc7fwi2p4
A Review: Comparative Analysis Of Different Categorical Data Clustering Ensemble Methods
2014
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This cluster ensemble is a good alternative approach for facing the cluster analysis problem. ...
The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. ...
Link based Clustering Ensemble Method (LCE) This link based cluster ensemble method denotes the discovery of unknown values in the cluster co-association matrix [25] . ...
doi:10.5281/zenodo.1336474
fatcat:7uxe2jhtnzhhjfp5gx5iwuoyni
A Comprehensive Review On Different Mixed Data Clustering Ensemble Methods
2014
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper shows the comparative analysis of different cluster ensemble methods along with their methodologies and salient features. ...
The main objective of the Cluster Ensemble is to aggregate the diverse clustering solutions in such a way to attain accuracy and also to improve the eminence the individual clustering algorithms. ...
Improved Link-Based Cluster Ensemble (ILCB) The Link-based method (LCE) is introduced in [15] , [16] to attend and use cluster ensemble associations to their true potential. ...
doi:10.5281/zenodo.1094744
fatcat:bv36lranvrcwpf54nkjlzutoou
Using Link-Based Consensus Clustering for Mixed-Type Data Analysis
2022
Computers Materials & Continua
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. ...
Given this insight, the paper introduces novel extensions of link-based cluster ensemble, LCE WCT and LCE WTQ that are accurate for analyzing mixed-type data. ...
Conclusion This paper has presented the novel extension of link-based consensus clustering to mixed-type data analysis. ...
doi:10.32604/cmc.2022.019776
fatcat:yfajxy2j2nh5zpn3bl25qaemty
Multi-source data fusion study in scientometrics
2017
Scientometrics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
The model and procedure can be divided into three parts: data type integration, fusion of data relations, and ensemble clustering. ...
To obtain a clearer and deeper analysis of the MSDF model, this paper further focuses on the application of MSDF in topic identification based on text analysis of scientific literatures. ...
With the continuous improvement of clustering analysis methods, an ensemble clustering algorithm can be introduced to merge a variety of clustering results into a final one, and therefore, it is crucial ...
doi:10.1007/s11192-017-2290-5
fatcat:xo2sofam6fbyrfparphqzjanni
Ensembling Solutions for Semi – Supervised Clusters
2018
International Journal for Research in Applied Science and Engineering Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
At that point, the arbitrary subspace based semi-regulated bunching troupe structure with an arrangement of proposed certainty factors is intended to address the second constraint and give more steady, ...
robust, and exact outcomes to perform the clustering process. ...
Traditional constrained clustering approaches have two limitations: (1) They do not consider how to make full use of must-link constraints and cannot-link constraints; (2) Some methods do not take into ...
doi:10.22214/ijraset.2018.3657
fatcat:3px3hpjvuzhxnpa3e2mizuxcva
Semi-supervised consensus clustering for gene expression data analysis
2014
BioData Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Lam-on N, Boongoen T, Garett S: LCE: a link-based cluster ensemble method for improved gene expression
data analysis. Bioinformatics 2010, 26(12):1513–1519.
5. ...
A well known consensus clustering algorithm, link-
based cluster ensemble (LCE) was introduced in [4]. ...
doi:10.1186/1756-0381-7-7
pmid:24920961
pmcid:PMC4036113
fatcat:5dcazjkcs5d23pos5rwqmzxs7a
Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database
2010
Journal of the American Society for Information Science and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. ...
The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. ...
The combination of link-based clustering with a textual approach was suggested as early as 1990 to improve the efficiency and usability of cocitation and coword analysis. ...
doi:10.1002/asi.21312
fatcat:fu5aca7gefdfvbni5sm6odjrlq
Comparative study of matrix refinement approaches for ensemble clustering
2013
Machine Learning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. ...
Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. ...
One is related to the generation (or selection) method that improves the quality of cluster ensembles. ...
doi:10.1007/s10994-013-5342-y
fatcat:znglu2ity5cutoza3h5xtwswaq
Semi-supervised Selective Affinity Propagation Ensemble Clustering with Active Constraints
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
And to solve the problem of random selection of pairwise constraints, an active learning strategy is applied to find most informative constraints based on the ensemble clustering result; these pairwise ...
The semi-supervised selective affinity propagation ensemble clustering with active constraints (SSAPEC) method combines affinity propagation (AP) clustering algorithm with ensemble clustering and incorporates ...
INTRODUCTION Clustering analysis is widely applied in many fields as a data analysis and processing method, including image processing [1] , financial fields [2] , biomedicine [3] , and others. ...
doi:10.1109/access.2020.2978404
fatcat:6xembf55qbgmbplmicjy4pxgni
Hybridization of Brownboost and Random Forest Tree with Gradient Free Optimization for Route Selection
2021
International Scientific Journal of Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
A novel link stability estimation technique called Hybridization of Brownboost Cluster and Random Forest Decision Tree with Optimized Route Selection (HBCRFDT-GORS) technique is introduced for increasing ...
Then the broken link is removed from the route path. ...
path between the clusters, '𝑤' denotes an amount of remaining time of the base cluster (𝑤 = 𝑏). ...
doi:10.47839/ijc.20.3.2286
fatcat:lsawjcthobho3fhwkw3ciixvsq
Multi-objective clustering ensemble for high-dimensional data based on Strength Pareto Evolutionary Algorithm (SPEA-II)
2015
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A recent clustering approach, clustering ensembles tries to derive an improved clustering solution based on previously generated different candidate clustering solutions. ...
In this research work, we present a new multi-objective clustering ensemble method based on Strength Pareto Evolutionary Algorithm (SPEA-II). ...
The first method is a single objective average-link clustering ensemble method based on link pairwise similarity matrices [25] . ...
doi:10.1109/dsaa.2015.7344795
dblp:conf/dsaa/WahidGA15
fatcat:gmyjaw2ltre3vdsel5ejy3l3dq
« Previous
Showing results 1 — 15 out of 70,657 results