Unsupervised learning by cluster quality optimization

López‐Rubio, Ezequiel; Palomo, Esteban J.; Ortega-Zamorano, Francisco

doi:10.1016/j.ins.2018.01.007

Cited by 19 publications

(11 citation statements)

References 35 publications

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“…The largest decrease in the within-cluster sum of squares indicates the optimal number of clusters (López-Rubio et al, 2018). Next, we used one-way ANOVAs and post-hoc comparisons with Bonferroni corrections to compare the clusters in experimentation skills (RQ2) and conceptual understanding (RQ3).…”

Section: Discussionmentioning

confidence: 99%

How do Students' Cognitive and Motivational Characteristics Relate to Experimentation Skills and Conceptual Understanding during Scientific Inquiry

Omarchevska¹,

Lachner²,

Jacob³

et al. 2023

Preprint

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Inquiry learning relies on students’ engagement, which is driven by their cognitive and motivational characteristics. The role of student characteristics for inquiry learning has been sparingly investigated and only from a variable-oriented perspective. In contrast, this study employed a person-centered approach to investigate the combined influence of students’ cognitive and motivational characteristics in relation to experimentation skills and conceptual understanding during guided inquiry learning. All students (N = 110, Mage = 12.07) attended a lesson about photosynthesis and worked on a structured inquiry task individually. Experimentation skills were measured by the proportion of controlled experiments that students conducted. Conceptual understanding comprised students’ written argumentation and conceptual knowledge posttest scores. We identified three distinct clusters, based on students’ prior knowledge, self-concept, and interest – Underestimating, Struggling, and Strong. Although Struggling students conducted a significantly higher proportion of controlled experiments than Strong students, Underestimating and Strong students outperformed them in the conceptual knowledge posttest. We found no differences in argumentation quality. These findings suggest that highly guided tasks might not be equally suitable for students with different levels of interest, prior knowledge, and self-concept and attest to the benefits of using person-oriented methods. Therefore, students’ motivation needs to be considered in addition to their prior knowledge when designing guidance for inquiry learning.

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Section: Discussionmentioning

confidence: 99%

How do Students' Cognitive and Motivational Characteristics Relate to Experimentation Skills and Conceptual Understanding during Scientific Inquiry

Omarchevska¹,

Lachner²,

Jacob³

et al. 2023

Preprint

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“…Unsupervised machine learning algorithms organize the data into a group of clusters. It describes its structure and gives complex data a simple, organized look for analysis (Ezequiel López-Rubio et al, 2018).…”

Section: MLmentioning

confidence: 99%

Recommender System Using Unsupervised Machine Learning for Satisfaction Surveys

Mbaye¹

2019

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; And Theory An

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Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of new technologies the piloting of these satisfaction surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the recommendation system and to make the system partially autonomous on decision-making (to less involving the recommendation engine). Our approach is divided into a) the recommendation process for decision-making, b) unsupervised ML and c) partial "empowerment" for decision-making.

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“…Clustering is the organization of unlabeled data into similarity groups [1,2,3]. Clustering has been widely used for many fields, including data mining [4,5], pattern recognition [6,7], and machine learning [8,9]. Clustering methods can be mainly categorized into two groups [10]: partitioning clustering and hierarchical clustering.…”

Section: Introductionmentioning

confidence: 99%

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering

Lina,

Zhongb,

Shu

2020

Preprint

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Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contain noise and are highly redundant. Moreover, most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because different views would present different geometric structures while admitting the same cluster structure. To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. Especially, a common cluster structure regularization is adopted to guarantee consistency among different views. An efficient algorithm based on an augmented Lagrangian multiplier is also developed to solve the associated optimization problem. Experiments conducted on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.

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Unsupervised learning by cluster quality optimization

Cited by 19 publications

References 35 publications

How do Students' Cognitive and Motivational Characteristics Relate to Experimentation Skills and Conceptual Understanding during Scientific Inquiry

How do Students' Cognitive and Motivational Characteristics Relate to Experimentation Skills and Conceptual Understanding during Scientific Inquiry

Recommender System Using Unsupervised Machine Learning for Satisfaction Surveys

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering

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