Clustering Evaluation in Feature Space

Nasser, Alissar; Hébert, Pierre-Alexandre; Hamad, Denis

doi:10.1007/978-3-540-74695-9_33

Cited by 3 publications

(3 citation statements)

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“…The non-linear version of the Davies & Bouldin (DB) validity index was first introduced by Nasser et al [19]. The index is used to evaluate the quality of clustering algorithms by plotting the values of the index against the number of clusters; a minimal value indicates the optimal number of clusters within the dataset.…”

Section: Kernel Davies and Bouldin Indexmentioning

confidence: 99%

“…A practical approach is to compare the outcomes of multiple runs with different k and choose the best one based on a predefined criterion [26]. 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%

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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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Section: Kernel Davies and Bouldin Indexmentioning

confidence: 99%

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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“…A clustering algorithm has to determine the appropriate number of clusters, as does for instance the Davies-Bouldin index [19], and to determine the measure that will permit to assign the elements into the said clusters [20]. PCA has been used as a clustering method among other techniques such as functional enrichment and expectation-maximization [21], kernel PCA [22], kernel PCA compared with the Davies-Bouldin index [20,23], Mercer kernelbased clustering [24], or spectral clustering [25]. Clustering has been applied to data mining [26], image processing [25], pattern recognition [27], self-organizing systems [28,29], and others.…”

Section: Introductionmentioning

confidence: 99%

A method for processing multivariate data in medical studies

Coubard¹

2013

Statistics in Medicine

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Traditional displays of principal component analyses lack readability to discriminate between putative clusters of variables or cases. Here the author proposes a method that clusterizes and visualizes variables or cases through principal component analyses thus facilitating their analysis. The method displays pre-determined clusters of variables or cases as urchins that each has a soma (the average point) and spines (the individual variables or cases). Through three examples in the field of neuropsychology, the author illustrates how urchins help examine the modularity of cognitive tasks on the one hand, and identify groups of healthy vs. brain-damaged participants on the other hand. Some of the data used in this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The urchin method was implemented in MATLAB, and the source code is available in the online Supporting information. Urchins can be useful in biomedical studies to identify distinct phenomena at first glance, each having several measures (clusters of variables) or distinct groups of participants (clusters of cases).

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