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

S Pavan Kumar Reddy, U Sesadri
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

Natthakan Iam On, Tossapon Boongoen, Nattawut Kongkotchawan
2014 International Journal of Collaborative Intelligence  
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

S. Sarumathi, N. Shanthi, M. Sharmila
2013 International Journal of Computer Applications  
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

M. Pavithra, R. M. S. Parvathi
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
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

S. Sarumathi, N. Shanthi, M. Sharmila
2014 Zenodo  
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

S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila
2014 Zenodo  
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

Tossapon Boongoen, Natthakan Iam-On
2022 Computers Materials & Continua  
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

Hai-Yun Xu, Zeng-Hui Yue, Chao Wang, Kun Dong, Hong-Shen Pang, Zhengbiao Han
2017 Scientometrics  
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

Viveka Priya
2018 International Journal for Research in Applied Science and Engineering Technology  
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

Yunli Wang, Youlian Pan
2014 BioData Mining  
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

Xinhai Liu, Shi Yu, Frizo Janssens, Wolfgang Glänzel, Yves Moreau, Bart De Moor
2010 Journal of the American Society for Information Science and Technology  
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

Natthakan Iam-On, Tossapon Boongoen
2013 Machine Learning  
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

Qi Lei, Ting Li
2020 IEEE Access  
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

P. Tamilselvi, T.N. Ravi
2021 International Scientific Journal of Computing  
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)

Abdul Wahid, Xiaoying Gao, Peter Andreae
2015 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)  
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
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