Enhancing test case reduction by k-means algorithm and elbow method

Pandey, Abhishek; Malviya, Anil Kumar

doi:10.26438/ijcse/v6i6.299303

Cited by 10 publications

(3 citation statements)

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“…The clustering of microbial communities was explored with the k -means algorithm which minimizes the error inside the groups and maximizes the distance between clusters. We considered the Euclidean distance metric in our analysis and then tried to use the elbow method to determine the optimum number of clusters [ 22 ]. In this method, the slow-down point denotes the optimum number of clusters.…”

Section: Methodsmentioning

confidence: 99%

The association between the pre-pregnancy vaginal microbiome and time-to-pregnancy: a Chinese pregnancy-planning cohort study

Xiang

Zhao

Yin

et al. 2022

BMC Med

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Background Although sexually transmitted infections are regarded as the main cause of tubal infertility, the association between the common vaginal microbiome and female fecundability has yet to be determined. The objective of this study was to find convincing evidence relating to the impact of the vaginal bacterial structure on the fecundability of women planning pregnancy. Methods We recruited women who took part in the Free Pre-pregnancy Health Examination Project from 13 June 2018 to 31 October 2018 (n = 89, phase I) and from 1 November 2018 to 30 May 2020 (n = 389, phase II). We collected pre-pregnancy vaginal swabs from each subject; then, we followed up each subject to acquire the pregnancy-planning outcome in 1 year. In phase I, 16S rRNA gene sequencing was performed to investigate the vaginal bacterial content between the pregnancy and non-pregnancy groups. These findings were verified in phase II by applying a quantitative real-time polymerase chain reaction for the measurement of the absolute abundance of specific species. Cox models were used to estimate fecundability ratios (FR) for each vaginal microbiome type. Results In phase I, 59.6% (53/89) of women became pregnant within 1 year. The principal coordinate analysis showed that the pre-pregnancy vaginal microbial community structures of the pregnant and non-pregnant groups were significantly different (PERMANOVA test, R2 = 0.025, P = 0.049). The abundance of the genus Lactobacillus in the pregnancy group was higher than that of the non-pregnant group (linear discriminant analysis effect size (LDA) > 4.0). The abundance of the genus Gardnerella in the non-pregnant group was higher than those in the pregnant group (LDA > 4.0). In phase II, female fecundability increased with higher absolute loads of Lactobacillus gasseri (quartile Q4 vs Q1, FR = 1.71, 95%CI 1.02–2.87) but decreased with higher absolute loads of Fannyhessea vaginae (Q4 vs Q1, FR = 0.62, 95%CI 0.38–1.00). Clustering analysis showed that the vaginal microbiome of type D (characterized by a higher abundance of Lactobacillus iners, a lower abundance of Lactobacillus crispatus and Lactobacillus gassri) was associated with a 55% reduction of fecundability (FR = 0.45, 95%CI 0.26–0.76) compared with type A (featuring three Lactobacillus species, low Gardnerella vaginalis and Fannyhessea vaginae abundance). Conclusions This cohort study demonstrated an association between the pre-pregnancy vaginal microbiome and female fecundability. A vaginal microbiome characterized by a higher abundance of L. iners and lower abundances of L. crispatus and L. gasseri appeared to be associated with a lower fecundability. Further research now needs to confirm whether manipulation of the vaginal microenvironment might improve human fecundability.

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Section: Methodsmentioning

confidence: 99%

The association between the pre-pregnancy vaginal microbiome and time-to-pregnancy: a Chinese pregnancy-planning cohort study

Xiang

Zhao

Yin

et al. 2022

BMC Med

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“…Metode Elbow adalah metode penentuan jumlah cluster atau nilai K terbaik secara tepat sebagai persentase dari hasil komparasi antara jumlah cluster membentuk sudut pada suatu titik [11], [12]. Jika nilai cluster pertama dengan nilai cluster kedua untuk sudut dalam grafik, atau jika nilainya paling banyak berkurang, jumlah nilai cluster sudah benar.…”

Section: Metode Elbowunclassified

Rekomendasi Restock Barang di Toko Pojok UMKM Menggunakan Algoritma K-Means Clustering

Salsabiela,

Kuncoro,

Subarkah

et al. 2024

J. Technol. Inform.

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Toko Pojok ialah toko yang menjual beraneka produk seperti makanan ringan dan minuman. Toko Pojok UMKM dalam pembukuan selama ini masih dilakukan secara manual dengan buku besar baik dalam pencatatan produk masuk maupun penjualan. Pembukuan dilakukan belum tersistem mengakibatkan data menumpuk, sehingga pemilik kesulitan dalam melihat stok yang terjual sehingga pengadaan barang ke supplier ada keterlambatan. Berdasarkan permasalahan tersebut penelitian ini dilakukan untuk merekomendasikan restock produk menggunakan algoritma K-Means dimana pengadaan barang dalam jumlah banyak, sedang dan sedikit. Penelitian ini menggunakan algoritma k-means clustering dengan 3 cluster. Di mana nilai k=3 merupakan nilai cluster terbaik dalam penelitian ini dengan performa dari cluster berdasarkan hasil Davies Bouldin Index sebesar 0.436, di mana cluster 1 ada 9 produk jumlah banyak dan masuk pengadaan barang, cluster 2 ada 19 produk jumlah sedang dan masuk pengadaan barang serta cluster 0 ada 110 produk jumlah sedikit masuk pengadaan barang.

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“…To understand the dyeing properties of the given dye combination on different fabric types, we performed clustering analysis to the points that locate in the 18-dimensional space. K-means algorithm was used for clustering [14], and a simple elbow plot (the number of clusters N vs. K-means score) was generated for better understanding the clustering performance (Figure 2) [15][16]. The K-means score is defined as the total Euclidean distance from individual points to the clustering centers, which are randomly initialized and updated following the standard K-means algorithm.…”

Section: Clustering Analysis For the Oloration Propertymentioning

confidence: 99%

Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics

Yin

Qin

2022

Frontiers in Artificial Intelligence and Applications

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Intelligent manufacturing for the fabric dyeing industry requires high-performance dyeing recipe recommendation systems. Nowadays, recommending dyeing recipes by mining dyeing manufacturing data has become a new direction for the development of recipe recommendation systems. As one of the indispensable parts in the system development, data pre-processing needs more than routine steps such as the removal of missing data and outliers. Considering that dyes can have very different coloration properties on different fabrics, dyeing manufacturing records for a given dye combination to different fabric types should be properly categorized before they are used for training regression models for dyeing recipe prediction. In this paper, we propose a simple but effective method for this categorization work. Our method uses conventional K-means clustering analysis to find fabric types that have similar coloration properties for a given dye combination. We have applied the method on a dye combination formed by Colvaceton reactive dye-navy blue CF (CRD-navy blue), Colvaceton reactive dye-bright red 3BSN150% (CRD-red) and Colvaceton reactive dye-yellow 3RS150% (CRD-yellow) on 28 different types of fabrics. We show that these 28 types of fabrics can be well categorized into 8 groups based on the coloration properties. Our proposed method can be listed as one of the standard data pre-processing steps in the development of data-mining based recipe recommendation systems.

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Enhancing test case reduction by k-means algorithm and elbow method

Cited by 10 publications

References 0 publications

The association between the pre-pregnancy vaginal microbiome and time-to-pregnancy: a Chinese pregnancy-planning cohort study

The association between the pre-pregnancy vaginal microbiome and time-to-pregnancy: a Chinese pregnancy-planning cohort study

Rekomendasi Restock Barang di Toko Pojok UMKM Menggunakan Algoritma K-Means Clustering

Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics

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