Cluster quality based performance evaluation of hierarchical clustering method

Nisha, .; Kaur, Puneet Jai

doi:10.1109/ngct.2015.7375201

Cited by 31 publications

(11 citation statements)

References 2 publications

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“…The PCs were used in the HCA to avoid collinearity of the input data and to enable the identification of multi-segment patterns. The HCA created a cluster tree (or dendrogram) using an agglomerative strategy, or a “bottom-up” approach, which consists of three steps: (1) a measure of dissimilarity between sets of subjects using the Euclidean distance, (2) subject linkage using the Ward’s minimum variance method ( Ward, 1963 ), and (3) optimal cluster determination based on the cluster division that rendered the highest average Silhouette index ( Rousseeuw, 1987 ; Arbelaitz et al, 2013 ; Nisha and Kaur, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis

Watari

Suda

Santos

et al. 2021

Front. Bioeng. Biotechnol.

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The purpose of this study is to identify homogenous subgroups of foot-ankle (FA) kinematic patterns among recreational runners and further investigate whether differences in baseline movement patterns can influence the mechanical responses to a foot-core exercise intervention program. This is a secondary analysis of data from 85 participants of a randomized controlled trial (clinicaltrials.gov – NCT02306148) investigating the effects of an exercise-based therapeutic approach focused on FA complex. A validated skin marker-based multi-segment foot model was used to acquire kinematic data during the stance phase of treadmill running. Kinematic features were extracted from the time-series data using a principal component analysis, and the reduced data served as input for a hierarchical cluster analysis to identify subgroups of FA movement patterns. FA angle time series were compared between identified clusters and the mechanical effects of the foot-core exercise intervention was assessed for each subgroup. Two clusters of FA running patterns were identified, with cluster 1 (n = 36) presenting a pattern of forefoot abduction, while cluster 2 (n = 49) displayed deviations in the proximal segments, with a rearfoot adduction and midfoot abduction throughout the stance phase of running. Data from 29 runners who completed the intervention protocol were analyzed after 8-weeks of foot-core exercises, resulting in changes mainly in cluster 1 (n = 16) in the transverse plane, in which we observed a reduction in the forefoot abduction, an increase in the rearfoot adduction and an approximation of their pattern to the runners in cluster 2 (n = 13). The findings of this study may help guide individual-centered treatment strategies, taking into account their initial mechanical patterns.

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Section: Methodsmentioning

confidence: 99%

Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis

Watari

Suda

Santos

et al. 2021

Front. Bioeng. Biotechnol.

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“…For the unsupervised classification, Clustering [9] will be used with the help of centroid-based algorithms (k-means) to see how the groups will be distributed in 3 configurations; first with the original data, then with a reduction of dimensionality using PCA and finally applying t-SNE.…”

Section: Model Learningmentioning

confidence: 99%

Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning Techniques

Cruz

Cristancho

Camargo

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Colombia has a diversity of genres in traditional music, which allows to express the richness of the Colombian culture according to the region. This musical diversity is the result of a mixture of African, native Indigenous, and European influences. Organizing large collections of songs is a time consuming task that requires that a human listens to fragments of audio to identify genre, singer, year, instruments and other relevant characteristics that allow to index the song dataset. This paper presents a method to automatically identify the genre of a Colombian song by means of its audio content. The method extracts audio features that are used to train a machine learning model that learns to classify the genre. The method was evaluated in a dataset of 180 musical pieces belonging to six folkloric Colombian music genres: Bambuco, Carranga, Cumbia, Joropo, Pasillo, and Vallenato. Results show that it is possible to automatically identify the music genre in spite of the complexity of Colombian rhythms reaching an average accuracy of 69%.

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“…degree of similarity, and they are different from objects, which are located outside the cluster [2]. In general, the techniques which are used in the clustering process can be classified into several categories: Partitioning Clustering, Hierarchical Clustering, Density-Based Clustering, Grid-Based Clustering, Model-Based Clustering, etc.…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

“…[3]. The main method which is discussed in this paper is Hierarchical Clustering where a number of formed clusters are mapped in the form of trees [2]. The process of Hierarchical Clustering is done by calculating the distance between sub clusters.…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

“…Sub-clusters with the closest distance will be combined into one cluster or group. The distance between sub-clusters can be done with various approaches such as Single Linkage, Complete Linkage, or Average Linkage [2]. Single Linkage focuses on the smallest distance or the closest neighbour between sub-clusters.…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

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Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Aisyah

Anggraini

Putriwijaya

et al. 2019

Jurnal Ilmu Komputer dan Informasi

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In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

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Cluster quality based performance evaluation of hierarchical clustering method

Cited by 31 publications

References 2 publications

Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis

Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis

Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning Techniques

Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

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