Investigating K-means and Kernel K-means Algorithms with Internal Validity Indices for Cluster Identification

Nasser, Alissar

doi:10.9734/jamcs/2019/45837

Cited by 4 publications

(3 citation statements)

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“…The results indicate that the total intra-cluster variations on the GLA-BRA-180 dataset are relatively higher between k = 1 and k = 5 before the autoencoder compression process, compared to the total intra-cluster variations after the autoencoder compression process for the same k values. This demonstrates the importance of the autoencoder compression process in reducing intra-cluster variations and, consequently, improving the k-hyperparameter tuning processes and the quality of clustering results, as stated by [115].…”

Section: Elbow Visualization Of the Gla-bra-180 High-dimensional Data...mentioning

confidence: 59%

“…MC Nemar's score [114] was used to determine if there was a statistically significant difference in the k-hyperparameter value before and after the autoencoder compression process on any high-dimensional dataset, specifically the GLA-BRA-180 dataset. Anova [115] was used to investigate whether there was a statistically significant difference between the performance of the existing techniques and the new technique in solving the challenges of a smooth elbow.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Gikera,

Mwaura,

Muuro

et al. 2023

Journal on Artificial Intelligence

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k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be inconsistent. Although various techniques for solving smooth elbow challenges exist, k-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue. In this paper, we have first reviewed the existing techniques for solving smooth elbow challenges. The identified research gaps are then utilized in the development of the new technique. The new technique, referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes, is then validated in high-dimensional space clustering. The optimal k-value, tuned by this technique using a voting scheme, is a trade-off between the number of clusters visualized in the autoencoder's latent space, k-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivativeat the elbow. Experimental results based on the Cochran's Q test, ANOVA, and McNemar's score indicate a relatively good performance of the newly developed technique in k-hyperparameter tuning. KEYWORDS k-hyperparameter tuning; high-dimensional; smooth elbow K-Means ArchitectureK-means is an iterative algorithm that aims to partition a dataset into a set of k non-overlapping groups of data points [9]. The k-hyperparameter is one of the most important hyperparameters to tune in k-means [10,11]. Tuning a machine learning model's hyperparameters has a significant effect on its performance [12]. In this subsection, we explore k-clusters, k-hyperparameters, and the traditional elbow method used to identify the optimal value for the k-hyperparameter. K-Means ClustersThe k-means clusters are the resulting data sub-groups generated by the popular unsupervised partitioning algorithm, known as the k-means clustering algorithm [13]. Cluster analysis using the k-means algorithm is an example of a k-means-based model that has been successfully applied in cluster analysis [14,15]. The k-clusters generated by these algorithms are usually composed of distinct non-overlapping groups of data points, aggregated together because they share specific similarities [16]. The data points within a particular cluster are similar, while the data points across different clusters are different [17]. Both the intra-cluster and inter-cluster data points are measured using a sum of squared distance-based metric [18,19]. For this reason, the original un-partitioned dataset is stan...

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Section: Elbow Visualization Of the Gla-bra-180 High-dimensional Data...mentioning

confidence: 59%

Section: Evaluation Metricsmentioning

confidence: 99%

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Gikera,

Mwaura,

Muuro

et al. 2023

Journal on Artificial Intelligence

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“…Several approaches have been proposed to choose the optimal value of K, in [19] we proposed to use the kernel Davies & Bouldin index to determine the optimal k value. In [27] we compare several internal validity indices to determine the optimal value of K clusters.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Security Detection in Audio Events: A Comparison of Classification Methods

Nasser

2020

JAMCS

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The security of public places is becoming important with the increased rate of violence and subversion. Recently, several types of research have been proposed to automatically detect abnormal behavior in public places like a car crash, violence or other hazardous events in an attempt to improve security and save lives. Furthermore, most of the researches are using supervised classifications techniques to classify the audio signals. This paper proposes the use of the kernel principal component analysis (KPCA) to reduce the number of MFCC features extracted from the audio signal and then apply an unsupervised classification algorithm. Moreover, this paper presents the results of several supervised and unsupervised classification methods for audio events detection and compares these results with the result of the proposed approach. Experiments are done using a real data set recorded at the mean of public transportation. The obtained results reveal that K-means on 2 KPCA components gave good results for triggering a true alarm as well as detecting a false alarm; where the percentages of false and missed alarms were 4.5% and 7.8% respectively; whereas these values were 0.8% and 9.3% respectively for kernel k-means. Notwithstanding the DNN network gave the best results with a false alarm rate of 0% and 1.4% missed alarm.

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Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

2021

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Investigating K-means and Kernel K-means Algorithms with Internal Validity Indices for Cluster Identification

Cited by 4 publications

References 0 publications

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Security Detection in Audio Events: A Comparison of Classification Methods

Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

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