A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm

Parvïn, Hamïd; Minaei-Bidgoli, Behrouz

doi:10.1007/s10044-013-0364-4

Cited by 61 publications

(31 citation statements)

References 25 publications

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“…The clustering ensemble approaches with homogenous clustering algorithms employ a same clustering algorithm during generation of the ensemble pool, that is, all partitions of the ensemble pool are generated by a same clustering algorithm. The partitions of the ensemble pool in homogenous clustering algorithms can be produced by one of the following subtypes: by employing different initializations of a given clustering algorithm , by employing different parameters (like different numbers of clusters) for data clustering using a same clustering algorithm , by employing different data projections for data clustering using a same clustering algorithm , by employing different subsets of dataset features for data clustering using a same clustering algorithm , by employing meta heuristic algorithms for data clustering , and by employing different datasets for data clustering using a same clustering algorithm. …”

Section: Related Workmentioning

confidence: 99%

“…The whole of empirical investigations are accomplished using Matlab2015. The proposed technique is evaluated against a portion of the best strategies in the field such as: Hybrid Bi‐Partite Graph Formulation ( HB _ PGF ) , Sim‐Rank Similarity ( SRS ) , Weighted‐Connected Triple ( W _ CT ) , Cluster Selection‐Evidence Accumulation Clustering ( CS _ EAC ) , Weighted‐Evidence Accumulation Clustering ( W _ EAC ) , Wisdom of Crowds Ensemble ( WCE ) , Graph Partitioning with Multi‐Granularity Link Analysis ( GPM _ GLA ) , and Two_level Co‐Association Matrix Ensemble ( TCAME ) , Elite Cluster Selection‐Evidence Accumulation Clustering ( ECS _ EAC ) , Cluster‐Level Weighting‐Graph Clustering ( CLW _ GC ) , and Robust Clustering Ensemble based on Iterative Fusion of base Clusters ( RCEIFC ) . These techniques utilize the default suggestions of parameters by their relating authors.…”

Section: Experimentationsmentioning

confidence: 99%

“…The Normalized Mutual Information Measure ( NMI ‐ M ) , F ‐Measure ( F ‐ M ) , Accuracy Measure ( A ‐ M ) , and Adjacent Rand Index Measure ( ARI ‐ M ) are used for the quality assessment of the consensus partitions obtained by different clustering ensemble approaches. Note that any of these measures takes two parameters (two partitions) as input and returns a real number, which is greater than or equal to zero and less than or equal to one.…”

Section: Experimentationsmentioning

confidence: 99%

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Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

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After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Experimentationsmentioning

confidence: 99%

Section: Experimentationsmentioning

confidence: 99%

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Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

Self Cite

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“…In fuzzy clustering, a novel method is developed for selecting the best fuzzy base clustering by defining a new fuzzy diversity measure and a fuzzy quality measure to prove the results have improved, compared with other fuzzy clustering ensemble methods . Furthermore, the fuzzy weighted locally adaptive clustering algorithm (FWLAC) was introduced to select features with more information than the others . Finding the best parameters with FWLAC is an issue although the performance of FWLAC is not dependent on finding these parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, two methods are suggested to solve this problem; either the entropy of the outputs is taken directly or an ensemble in the unstable area is taken. To obtain better results than the full ensemble, the parameters must take place in the unstable area . One article proposed two large‐scale clustering algorithms: ultrascalable spectral clustering (U‐SPEC) and ultrascalable ensemble clustering (U‐SENC) .…”

Section: Introductionmentioning

confidence: 99%

Ensemble cluster pruning via convex‐concave programming

Özöğür‐Akyüz

Otar

Ataş

2020

Computational Intelligence

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Summary Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.

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Double‐weighted fuzzy clustering with samples and generalized entropy features

Lin

Chen

et al. 2020

Concurrency and Computation

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Summary For the dataset with different sample contributions and different feature importance, it is difficult to acquire a proper cluster structure that covers the entire features of the sample set. To improve the clustering result, a novel weighted fuzzy clustering algorithm based on both samples and generalized entropy features, called SGEF‐WFCM, is proposed in this article, among which a new objective function is developed on the basis of feature‐weighted generalized entropy regularization with a double‐weighting strategy of samples and features, the weighted coefficients of the features to each cluster, as well as the importance of the samples to the cluster are calculated dynamically, to obtain a better clustering result. Finally, experiments on both synthetic datasets and real‐world datasets from UCI are employed to verify the performance of the proposed SGEF‐WFCM algorithm. The results show that SGEF‐WFCM is superior to the conventional FCM algorithm in both the effectiveness and the usefulness during practices.

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A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm

Cited by 61 publications

References 25 publications

Dependability‐based cluster weighting in clustering ensemble

Dependability‐based cluster weighting in clustering ensemble

Ensemble cluster pruning via convex‐concave programming

Double‐weighted fuzzy clustering with samples and generalized entropy features

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