A comprehensive study of clustering ensemble weighting based on cluster quality and diversity

Nazari, Ahmad; Dehghan, Ayob; Nejatian, Samad; Rezaie, Vahideh; Parvïn, Hamïd

doi:10.1007/s10044-017-0676-x

Cited by 53 publications

(27 citation statements)

References 58 publications

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“…(7) Hybrid clustering algorithms such as ensemble clustering [22][23][24] combine at least two kinds of the clustering algorithms mentioned above to get higher quality clustering results. Also, ensemble clustering algorithms using various strategies [25][26][27][28][29][30][31][32][33][34] to break through the limitations of base clustering algorithms have been increasingly popular in recent years. But these kinds of algorithms may have high time complexity.…”

Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.

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Section: Data Object Clustering Methodsmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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“…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%

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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“…The approach has been performed on different temporal datasets, validating Yang and Jiang method, which accomplishes promising efficiency for temporal data clustering analysis. In addition, a new clustering ensemble scheme which is built on the idea of cluster‐level weighting has been introduced . The certainty measurement of an ensemble is taken as its reliability and calculated according to the gradual growth of the ensemble.…”

Section: Introductionmentioning

confidence: 99%

“…The certainty measurement of an ensemble is taken as its reliability and calculated according to the gradual growth of the ensemble. Subsequently, the method tries to select the best clusters and assign a weight to each selected cluster to generate the final ensemble . In Nazari et al, the idea of using using optimization techniques to choose the optimized subset of clusters is described as valuable future work.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a new clustering ensemble scheme which is built on the idea of cluster-level weighting has been introduced. 11 The certainty measurement of an ensemble is taken as its reliability and calculated according to the gradual growth of the ensemble. Subsequently, the method tries to select the best clusters and assign a weight to each selected cluster to generate the final ensemble.…”

Section: Introductionmentioning

confidence: 99%

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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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A comprehensive study of clustering ensemble weighting based on cluster quality and diversity

Cited by 53 publications

References 58 publications

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Dependability‐based cluster weighting in clustering ensemble

Ensemble cluster pruning via convex‐concave programming

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