A Weighted Object-Cluster Association-Based Ensemble Method for Clustering Undergraduate Students

Vo, Chau; Nguyen, Phung Hua

doi:10.1007/978-3-319-75417-8_55

Cited by 3 publications

(17 citation statements)

References 12 publications

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“…We name the proposed method kWOCA. It is an extended version of WOCA in Vo and Nguyen (2018) and thus, inherits all the characteristics of WOCA. In particular, kWOCA also follows the pair-wise co-occurrence-based approach.…”

Section: The Proposed Ensemble Methodsmentioning

confidence: 99%

“…Many existing works have been proposed with many expected results and different clustering algorithms. Some of them are listed as: Adjei, Ostrow, Erickson, and Heffernan (2017) discovered similar student behaviours, Bresfelean, Bresfelean, and Ghisoiu (2008) aimed at similar profiles, and (Jovanovic, Vukicevic, Milovanovic, & Minovic, 2012;Kerr & Chung, 2012;Vo & Nguyen, 2018) focussed on similar performance. As for clustering algorithms, many popular existing algorithms: k-means, fuzzy c-means, spectral clustering, Self-Organizing Map, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, it can achieve the quality of the clusters to some certain extent due to its Gaussian spherical assumption for the resulting clusters. In addition, it is noted that except (Vo & Nguyen, 2018) with the proposed WOCA ensemble method, none of the existing educational data clustering works has considered the resulting clusters of higher quality with any ensemble method. As one of the first works advancing the educational data clustering task with an ensemble method for better clusters, more robustness, and knowledge reuse (Topchy, Jain, & Punch, 2005;Zhou, 2012), our work concentrates on the development of an extended version of WOCA in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…As for (Huang, Wang, & Lai, 2017;Ren, Domeniconi, Zhang, & Yu, 2016;Zhong, Yue, Zhang, & Lei, 2015) based on a co-association matrix that captured the co-occurrence of the objects in the same cluster, more information has been added into each value in the matrix or into each object in terms of weights. Detailed review about these works was given in Vo and Nguyen (2018). By contrast, our work approaches the task with an object-cluster association matrix.…”

Section: Introductionmentioning

confidence: 99%

“…A higher dimensional feature space is of our interest so that the separation between the objects and that between the clusters can be well determined. Similar to ours, Liu, Wu, Liu, Tao, and Fu (2017), Pattanodom, Iam-On, and Boongoen (2016), and Wu, Liu, Xiong, Cao, and Chen (2015) used an object-cluster association matrix, based on k-means as reviewed in Vo and Nguyen (2018). For Liu et al (2017), weighted k-means was considered to avoid the complexity of spectral ensemble clustering with spectral clustering (Ng, Jordan, & Weiss, 2002).…”

Section: Introductionmentioning

confidence: 99%

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A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Nguyen

2019

Journal of Information and Telecommunication

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Exploiting single models for a better ensemble one is popular for a classification or clustering task. For educational data clustering, our work proposes a kernel-induced weighted object-cluster association ensemble method, named kWOCA, to discover the inherent structure of the data and return the student clusters of higher quality. kWOCA is an advanced version that can automatically determine the number of desired clusters based on Bayesian Information Criterion. It also conducts consensus clustering in the feature space for non-linearly separated clusters where the discrimination between the objects is captured from the base clusterings. Besides, it encodes the differences between the clusters in each base clustering and those between the base clusterings in the ensemble when making a synthesis of the base clusterings. A kernel-induced weighted object-cluster association matrix is defined to store such rich information. Using this matrix, kWOCA outperforms the existing clustering ensemble methods. Experimental results on the real educational data sets and the benchmark Iris data set show the better effectiveness of kWOCA with higher Normalized Mutual Information values. As a result, groups of the most similar students based on their study performance can be discovered better. These resulting student clusters can be then further analyzed for academic affairs.

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Section: The Proposed Ensemble Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Nguyen

2019

Journal of Information and Telecommunication

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A Review of Clustering Models in Educational Data Science Toward Fairness-Aware Learning

Quy

Friege

Ntoutsi

2023

Big Data Management

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A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

Li,

Xu,

et al. 2024

Neural Process Lett

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Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.

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A Weighted Object-Cluster Association-Based Ensemble Method for Clustering Undergraduate Students

Cited by 3 publications

References 12 publications

A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

A Review of Clustering Models in Educational Data Science Toward Fairness-Aware Learning

A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

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