Clustering ensemble selection considering quality and diversity

Abbasi, Sadrollah; Nejatian, Samad; Parvïn, Hamïd; Rezaie, Vahideh; Bagherifard, Karamolah

doi:10.1007/s10462-018-9642-2

Cited by 80 publications

(38 citation statements)

References 66 publications

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“…It should also be noted that there are other studies comparing the performance of heuristic methods for optimization problems. [68][69][70] In addition, regarding the study of Abbasi et al, 52 the ENMI is stated as the best choice for the cluster evaluation which can be adapted to the proposed model in this study for a future work.…”

Section: Conclusion and Discussionmentioning

confidence: 96%

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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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Section: Conclusion and Discussionmentioning

confidence: 96%

“…This method has been shown to perform better than the NMI criterion . In addition to Alizadeh et al, a new study claims that edited normalized mutual information (ENMI), which is derived from a subset of total primary spurious clusters, performs better than NMI for cluster evaluation …”

Section: Background Materialsmentioning

confidence: 99%

Ensemble cluster pruning via convex‐concave programming

Özöğür‐Akyüz

Otar

Ataş

2020

Computational Intelligence

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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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“…Bagherinia et al have introduced an original fuzzy clustering ensemble framework in which effect of the diversity and quality of base clusters were studied [43]. In addition to Alizadeh et al [44,45], a new study claims that edited NMI (ENMI), which is derived from a subset of total primary spurious clusters, performs better than NMI for cluster evaluation [46].…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Locally Reliable Cluster Solutions

et al. 2020

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Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.

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Clustering ensemble selection considering quality and diversity

Cited by 80 publications

References 66 publications

Ensemble cluster pruning via convex‐concave programming

Ensemble cluster pruning via convex‐concave programming

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

An Ensemble of Locally Reliable Cluster Solutions

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