CLUSTER ENSEMBLE SELECTION

Average Cluster Consistency for Cluster Ensemble Selection

Communications in Computer and Information Science - Knowledge Discovery, Knowlege Engineering and Knowledge Management ◽

10.1007/978-3-642-19032-2_10 ◽

2011 ◽

pp. 133-148 ◽

Cited By ~ 1

Author(s):

F. Jorge F. Duarte ◽

João M. M. Duarte ◽

Ana L. N. Fred ◽

M. Fátima C. Rodrigues

Keyword(s):

Cluster Ensemble ◽

Ensemble Selection ◽

Average Cluster

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Statistical Analysis and Data Mining The ASA Data Science Journal ◽

10.1002/sam.10008 ◽

2008 ◽

Vol 1 (3) ◽

pp. 128-141 ◽

Cited By ~ 95

Author(s):

Xiaoli Z. Fern ◽

Wei Lin

Keyword(s):

Cluster Ensemble ◽

Ensemble Selection

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Hierarchical cluster ensemble selection

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2014.12.005 ◽

2015 ◽

Vol 39 ◽

pp. 146-156 ◽

Cited By ~ 26

Author(s):

Ebrahim Akbari ◽

Halina Mohamed Dahlan ◽

Roliana Ibrahim ◽

Hosein Alizadeh

Keyword(s):

Hierarchical Cluster ◽

Cluster Ensemble ◽

Ensemble Selection

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Social Network Optimization for Cluster Ensemble Selection

Fundamenta Informaticae ◽

10.3233/fi-2020-1964 ◽

2020 ◽

Vol 176 (1) ◽

pp. 79-102

Author(s):

Chenyue Zhao ◽

Hosein Alizadeh ◽

Behrouz Minaei ◽

Majid Mohamadpoor ◽

Hamid Parvin ◽

...

Keyword(s):

Large Scale ◽

Cluster Structure ◽

Ensemble Methods ◽

Quadratic Program ◽

Maximization Problem ◽

Similarity Matrix ◽

Cluster Ensemble ◽

Ensemble Selection ◽

Consensus Functions ◽

Consensus Partition

This paper studies the cluster ensemble selection problem for unsupervised learning. Given a large ensemble of clustering solutions, our goal is to select a subset of solutions to form a smaller yet better performing cluster ensemble than using all available solutions. The common way of aggregating the chosen solutions is accumulating the information of the selected results to a similarity matrix. This paper suggests transforming the similarity matrix to a modularity matrix and then applying a new consensus function which optimizes modularity measure in it. We represent the modularity maximization problem as a 0-1 quadratic program which can be exactly solved for small datasets. We also established a new greedy algorithm, namely sum linkage, to optimize the objective function specially for large scale datasets in a very short time. We show that the proposed consensus partition gets much closer to the actual cluster structure than the partitions obtained from the direct application of common cluster ensemble methods. The promising results compared with other most cited consensus functions show the excellent efficiency of the proposed method.

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Cluster ensemble selection using balanced normalized mutual information

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191531 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3033-3055

Author(s):

Zecong Wang ◽

Hamid Parvin ◽

Sultan Noman Qasem ◽

Bui Anh Tuan ◽

Kim-Hung Pho

Keyword(s):

Mutual Information ◽

Main Idea ◽

Ensemble Methods ◽

Cluster Ensemble ◽

Normalized Mutual Information ◽

Intermediate Space ◽

Ensemble Approach ◽

Innovative Cluster ◽

Aggregation Functions ◽

Ensemble Selection

A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach.

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