Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth

Patil, C. Y.; Baidari, Ishwar

doi:10.1007/s41019-019-0091-y

Cited by 72 publications

(28 citation statements)

References 16 publications

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“…After collecting and transforming data, we used the method of selecting a number of clusters [26] to get the appropriate number of clusters K = 5. The obtained experiment on EFM results is illustrated in Table 4 with five clusters such as: C1, C2, C3, C4 and C5 below: From the clustering results in Table 4, we could see that the blended learning method was chosen by the majority of students in all different levels of training.…”

Section: Experiments On Efm Resultsmentioning

confidence: 99%

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.

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Section: Experiments On Efm Resultsmentioning

confidence: 99%

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

2020

Education Sciences

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“…Determining the optimal number of clusters in a data set is a fundamental problem in partitioning clustering, such as K-means clustering, which allows the user to define the number of clusters K to be generated. The possible number of clusters is rather arbitrary and is determined by the method used for measuring similarities and the parameters used for partitioning [1]. There are many clustering algorithms used for the group the similar objects in many domains such as the Medical domain, Education domain, Governance domain, etc.…”

Section: Introductionmentioning

confidence: 99%

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Raj¹,

Vidyaathulasiraman²

2021

IJACSA

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Learning is appropriate when the learners are grouped and facilitated to learn according to their learning style and at their own pace. Elaborate researches have been proposed to categorize learners based on various e-learning parameters. Most of these researches have deployed the clustering principles for grouping eLearners, and in particular, they have utilized K-Medoid principle for better clustering. In the classical K-Medoid algorithm, predicting or determining the value of K is critical, two methods namely the Elbow and Silhouette methods are widely applied. In this paper, we experiment with the application of both these methods to determine the value of K for clustering eLearners in K-Medoid and prove that Silhouette method best predicts the value of K.

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“…Another challenge to be addressed in future works is to improve the way the optimal number of superpixels is extracted. In this research, we selected the number of superpixels to minimize the intra-cluster distance, but more sophisticated methods can be used in the future to reduce the number of superpixels while retaining the most important information from each image [36]. In this research, we overestimated the number of superpixel centroids in order to ensure proper extraction of spectral features from the HS image.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples

et al. 2020

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The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.

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Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth

Cited by 72 publications

References 16 publications

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples

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