Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Syakur, Muhammad Ali; Khotimah, Bain Khusnul; Rochman, Eka Mala Sari; Satoto, Budi Dwi

doi:10.1088/1757-899x/336/1/012017

Cited by 658 publications

(380 citation statements)

References 4 publications

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“…The differences of relative abundance (% OTU) of the four major phyla and of the Firmicutes-to-Bacteroidetes (F/B) ratio, which are the two major phyla in human gut microbiota and are known to be modulated by diet [9,11] Enterotypes of gut microbiota in healthy Korean adults were explored by a modified method to determine enterotype discovery (9) with a combination of principal coordinate analysis (PCoA) based on the weighted UniFrac distance matrix as a between-sample (β-) diversity index, and then k-means cluster analysis based on the PCoA scores of the first two principal coordinates (PCos). The optimal number of clusters was determined by visual inspection of clusters derived by three different methods -Elbow [43], Silhouette [44] and Gap statistic [45]…”

Section: Discussionmentioning

confidence: 99%

Taxonomic composition and diversity of the gut microbiota in relation to habitual diet in Korean adults

Noh

Jang

Kim

et al. 2020

Preprint

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Background: Little is known of the relationship between the Korean habitual diet and gut microbiota composition. We investigated associations of habitual dietary intake of foods and nutrients with the taxonomic composition and diversity of gut microbiota in 222 Korean adults aged 18-58 years in a cross-sectional study. Gut microbial taxonomic composition and diversity data were obtained by 16S rRNA gene sequencing of bacterial DNA extracted from fecal samples. Habitual diet for the previous year was collected by a validated food frequency questionnaire. Correlations between intakes of food and nutrients and gut microbial taxonomic composition were examined with adjustment for sex, age, body mass index, dietary supplement, smoking status, and sample batch. Specific dietary patterns associated with α-diversity were identified by reduced rank regression. Enterotypes of gut microbiota were explored by principal coordinate analysis based on β-diversity.Results: The intakes of vegetables, fermented legumes, and potatoes were positively associated with the Firmicutes-to-Bacteroidetes (F/B) ratio, while the intakes of noodle products and non-alcoholic beverages were inversely related to the F/B ratio (all P<0.05). A dietary pattern associated with higher α-diversity (HiαDP) was characterized by greater intakes of fermented legumes, vegetables, potatoes, tea, and fruit/fruit juice and lower intakes of non-alcoholic beverages. Among three different enterotypes identified based on the β-diversity, the Ruminococcus enterotype had higher scores of the HiαDP and was more strongly associated with intakes of vegetables and nuts/seeds, compared to the two other enterotypes. Conclusions: We conclude that the habitual diet in Korean adults was associated with gut microbial taxonomic composition and diversity. A higher intake of plant-based and fermented foods was associated with distinct gut microbial enterotypes in Korean adults.

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Section: Discussionmentioning

confidence: 99%

Taxonomic composition and diversity of the gut microbiota in relation to habitual diet in Korean adults

Noh

Jang

Kim

et al. 2020

Preprint

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“…where m represents the number of the data; x (i) is one sample and u c i is the corresponding closest cluster center of this sample; K represents the number of cluster centers and it is a pre-defined hyper-parameter that we can set it from two to eight. Finally, the Elbow Method [35] is used to determine the appropriate number of cluster centers K which also is the number of the adaptive aspect ratios of anchors here, and it is a great tradeoff between high recall and complexity of the neural network model. For the scales of anchors, we use the mini-batch k-means algorithm with the Intersection of Union (IoU) distance [3] instead of the aforementioned Euclidean distance to obtain the adaptive settings.…”

Section: Adaptive Anchorsmentioning

confidence: 99%

“…Then, we select the cluster centroids corresponding to the minimum of average IoU distance. With the various K and the corresponding minimum of average IoU distance, Elbow Method [35] is also used to determine the appropriate K. At this time, the cluster centroids represent the width and height of the prior anchors, and the scales of anchors can be acquired by calculating the side of the square which has the same area as the produced prior anchors. Furthermore, the mini-batch k-means algorithm is not only simple and liable to implement, but also can greatly promote the detection performance.…”

Section: Adaptive Anchorsmentioning

confidence: 99%

Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

Guo¹,

Li²,

Gong³

et al. 2020

Remote Sensing

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Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.Remote Sens. 2020, 12, 784 2 of 22 high-level semantic information at each scale. This structure displays an obvious improvement as a common feature extractor in some practical applications. However, since large-scale objects are usually produced and predicted in the deeper convolution layers of the FPN, the boundaries of these objects might be too fuzzy to obtain accurate regression. Furthermore, the FPN usually predicts small-scale objects in the shallower layers with low semantic information which might not be enough to identify the class of the objects. The designer of the FPN has been aware of this problem and adopted a top-down structure with lateral connections to fuse shallow layers and high-level semantic information to relieve it. However, if the small-scale objects disappear in the deep convolution layers, the context information cues will disappear at the same time.

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“…For analyses of stereotyped movements (K-means clustering), we generated scripts which implemented supervised machine learning packages from sci-kit learn (sklearn). We used the elbow method in concert with the silhouette coefficient (described below) to select the number of clusters (movement types) that a given forelimb movement could be assigned to (Supplemental Figure 2) (Syakur et al, 2018;Zhou & Gao, 2014). For the elbow method, the x-value at which exponential decay ceases (i.e at the elbow joint of the line graph) estimated the optimal number of clusters to use for a given dataset.…”

Section: Automated Video Analysismentioning

confidence: 99%

Performance and behavioral flexibility on a complex motor task depend on available sensory inputs in early blind and sighted short-tailed opossums

Englund

Faridjoo

Iyer

et al. 2020

Preprint

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The early loss of vision results in a reorganized visual cortex that processes tactile and auditory inputs. Recent studies in the short-tailed opossum (Monodelphis domestica) found that the connections and response properties of neurons in somatosensory cortex of early blind animals are also altered. While research in humans and other mammals shows that early vision loss leads to heightened abilities on discrimination tasks involving the spared senses, if and how this superior discrimination leads to adaptive sensorimotor behavior has yet to be determined. Moreover, little is known about the extent to which blind animals rely on the spared senses. Here, we tested early blind opossums on a sensorimotor task involving somatosensation and found that they had increased limb placement accuracy. However, increased reliance on tactile inputs in early blind animals resulted in greater deficits in limb placement and behavioral flexibility when the whiskers were trimmed.

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Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Cited by 658 publications

References 4 publications

Taxonomic composition and diversity of the gut microbiota in relation to habitual diet in Korean adults

Taxonomic composition and diversity of the gut microbiota in relation to habitual diet in Korean adults

Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

Performance and behavioral flexibility on a complex motor task depend on available sensory inputs in early blind and sighted short-tailed opossums

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