scholarly journals Metode Boost-K-means untuk Clustering Puskesmas berdasarkan Persentase Bayi yang Diimunisasi

2020 ◽  
Vol 4 (2) ◽  
pp. 89
Author(s):  
Ahmad Irfan Abdullah ◽  
Edi Winarko ◽  
Aina Musdholifah
Keyword(s):  
Silhouette Index

Dinas Kesehatan Kabupaten/Kota adalah satuan kerja pemerintahan daerah kabupaten/kota yang bertanggung jawab menyelenggarakan urusan pemerintahan dalam bidang kesehatan di kabupaten/kota. Pelayanan kesehatan adalah upaya yang diberikan oleh Puskesmas kepada masyarakat, mencakup perencanaan, pelaksanaan, evaluasi, pencatatan, pelaporan, dan dituangkan dalam suatu sistem. Pada penelitian ini, akan digunakan data persentase bayi yang diimunisasi yang merupakan salah satu layanan dari Puskesmas. Pelayanan imunisasi ini merupakan pelayanan imunisasi dasar meliputi BCG, DPT/HB1-3, polio 1-4 dan campak. Data persentase bayi yang diimunisasi belum memiliki pengelompokan sehingga pada penelitian ini akan diterapkan metode clustering untuk melakukan pengelompokan Puskesmas berdasarkan persentase bayi yang diimunisasi. Data persentase bayi dari masing-masing Puskesmas dijadikan data uji yang akan diterapkan pada proses multi-clustering dengan metode boost-clustering. Output dari penerapan metode ini akan dibandingkan dengan output metode clustering dasar k-means, hasil clustering akan diukur menggunakan metode silhouette index. Evaluasi menggunakan metode silhouette index dilakukan pada dataset puskesmas. Analisis dilakukan dengan melihat hasil evauasi dataset yang sudah diimplementasikan kedalam algoritma cluster dasar k-means dan algoritma multiclustering boost-k-means. Berdasarkan hasil evaluasi, diperoleh nilai silhouette index 0,798102756 untuk k-means dan 0,789901932 untuk boost-k-means, dengan ini algoritma yang diusulkan memiliki kualitas hasil clustering minimal sama atau lebih baik dari single clustering k-means dengan jumlah iterasi yang lebih sedikit

scholarly journals Medoid-based shadow value validation and visualization

2019 ◽  
Vol 5 (2) ◽  
pp. 76
Author(s):  
Weksi Budiaji
Keyword(s):  
Clustering Algorithm ◽  
Unique Property ◽  
Network Visualization ◽  
Visualization Technique ◽  
Internal Validation ◽  
Neighborhood Graph ◽  
Silhouette Index ◽  
Shadow Value

A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed.  Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV.

scholarly journals Mapping similarities in temporal parking occupancy behavior based on city-wide parking meter data

2018 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
Fabian Bock ◽  
Karen Xia ◽  
Monika Sester
Keyword(s):  
Similarity Measure ◽  
Historical Data ◽  
Parking Space ◽  
Number Of Clusters ◽  
Occupancy Patterns ◽  
Silhouette Index ◽  
The Mean ◽  
Occupancy Behavior ◽  
Maximum Occupancy ◽  
Repetitive Pattern

The search for a parking space is a severe and stressful problem for drivers in many cities. The provision of maps with parking space occupancy information assists drivers in avoiding the most crowded roads at certain times. Since parking occupancy reveals a repetitive pattern per day and per week, typical parking occupancy patterns can be extracted from historical data.<br> In this paper, we analyze city-wide parking meter data from Hannover, Germany, for a full year. We describe an approach of clustering these parking meters to reduce the complexity of this parking occupancy information and to reveal areas with similar parking behavior. The parking occupancy at every parking meter is derived from a timestamp of ticket payment and the validity period of the parking tickets. The similarity of the parking meters is computed as the mean-squared deviation of the average daily patterns in parking occupancy at the parking meters. Based on this similarity measure, a hierarchical clustering is applied. The number of clusters is determined with the Davies-Bouldin Index and the Silhouette Index.<br> Results show that, after extensive data cleansing, the clustering leads to three clusters representing typical parking occupancy day patterns. Those clusters differ mainly in the hour of the maximum occupancy. In addition, the lo-cations of parking meter clusters, computed only based on temporal similarity, also show clear spatial distinctions from other clusters.

scholarly journals Adaptive Unified Differential Evolution for Clustering

2018 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Maulida Ayu Fitriani ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Differential Evolution ◽  
Fitness Function ◽  
Single Mutation ◽  
Clustering Methods ◽  
Mutation Strategy ◽  
Clustering Quality ◽  
Silhouette Index ◽  
Mutation Strategies ◽  
Time Required

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1.

O-091 Semen microbiota in patients with astenozoospermia and healthy controls: cluster analysis of real-time PCR data

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
E Panacheva ◽  
D Pochernikov ◽  
E Voroshilina
Keyword(s):  
Cluster Analysis ◽  
Real Time ◽  
Real Time Pcr ◽  
Detection Rate ◽  
Bacterial Load ◽  
Rrna Gene ◽  
Rt Pcr ◽  
Silhouette Index ◽  
Enterococcus Spp ◽  
Lactobacillus Spp

Abstract Study question What are the differences in the semen microbiota composition of patients with asthenozoospermia and normospermia according to cluster analysis of PCR data? Summary answer The detection rate of 4 stable semen microbiota clusters and the dominant bacteria groups varied in patients with asthenozoospermia and normospermia. What is known already Most of the research dedicated to analyzing normal and pathological semen microbiota is based on 16S rRNA gene specific Next generation sequencing (NGS). It has shown that microbiota is represented by polymicrobial communities (clusters) that consist of microorganisms from different genera and bacteria phyla. Despite it being highly informative, NGS has several weaknesses: complex sample preparation, difficult sample intake control, long analysis process, complicated results interpretation, high cost of equipment and reagents. These factors make it virtually impossible to use this approach in routine medical practice. Quantitative real-time PCR (RT-PCR) is far more suitable for this. Study design, size, duration Patients included in the study (n = 301) came to the “Garmonia” Medical Center (Yekaterinburg, Russia) either seeking preconception care or for infertility treatment. Depending on the spermiogram results, they were divided into two groups. Group 1 (n = 171) — asthenozoospermia, Group 2 (n = 130) — normospermia. Participants/materials, setting, methods Semen microbiota was analyzed using RT-PCR kit Androflor (DNA-Technology, Russia). Cluster analysis was performed for 201 samples with the total bacterial load (TBL) of at least 103 GE/ml (asthenozoospermia = 96, normospermia = 105). Cluster analysis was conducted using the k-means ++ algorithm, scikit-learn. The Silhouette index and the Davies–Bouldin index (DBI) were used to confirm the stability of clusters. Main results and the role of chance Both in the samples with normospermia and asthenozoospermia, four stable microbiota clusters were distinguished. Cluster I was characterized by the prevalence of obligate anaerobes, Lactobacillus spp. were prevalent in Cluster II, Gram-positive facultative anaerobes were prevalent in Cluster III, Enterobacteriaceae/Enterococcus spp. were prevalent in Cluster IV. Cluster I was detected the most often in both groups. However, in normospermia it was represented by various obligate anaerobes without pronounced quantitative predominance of any bacteria group. In samples with asthenozoospermia one of the bacteria groups were prevalent in Cluster I: Bacteroides spp./Porphyromonas spp./Prevotella spp., Peptostreptococcus spp./Parvimonas spp. or Eubacterium spp. In samples with asthenozoospermia Cluster II was characterized by the prevalence of Lactobacillus spp., while in samples with normospermia other bacteria groups were present along with lactobacilli, mainly obligate anaerobes. In samples with normospermia Corynebacterium spp. and Streptococcus spp., typical of normal microbiota of male UGT, were prevalent in Cluster III. In samples with asthenozoospermia Cluster III were characterized by the prevalence of Staphylococcus spp. In samples with asthenozoospermia Lactobacillus spp was present in Cluster IV along with Enterobacteriaceae/Enterococcus spp., which was not typical of the samples with normospermia. Limitations, reasons for caution Cluster analysis was not conducted for the samples with TBL lower than 103 GE/ml, since their results were incompatible with the data received for the negative control samples. Wider implications of the findings Further research could determine the detection rate of the described bacterial clusters in semen with other pathologies. Establishing the relationship between the characteristics of semen microbiota and infertility in men might allow the development of new algorithms for treating patients with reproductive disorders, depending on the composition of semen microbiota. Trial registration number not applicable

VP135 Clustering Surgical Indicators And Predictors Of Catastrophic Expenses

2017 ◽  
Vol 33 (S1) ◽  
pp. 210-211
Author(s):  
Songul Cinaroglu ◽  
Onur Baser
Keyword(s):  
Medical Imaging ◽  
General Health ◽  
Surgical Care ◽  
The Third ◽  
Surgical Workforce ◽  
Silhouette Index ◽  
The World ◽  
Country Differences ◽  
Cross Country ◽  
Risk Predictors

INTRODUCTION:Increasing access to surgical care is crucial in improving the general health status of a population. Despite studies indicating the cross-country differences of general health indicators, there is a scarcity of knowledge focusing on the cross-country differences of surgical indicators. This study aims to classify countries according to surgical care indicators and identify risk predictors of catastrophic surgical care expenditures.METHODS:For this study, data were used from the World Health Organization and the World Bank on 177 countries. The following variable groups were chosen: total density of medical imaging technologies, surgical workforce distribution, number of surgical procedures, and risk of catastrophic surgical care expenditures. The k-means clustering algorithm was used to classify countries according to the surgical indicators. The optimal number of clusters was determined with a within-cluster sum of squares and a scree plot. A Silhouette index was used to examine clustering performance, and a random forest decision tree approach was used to determine risk predictors of catastrophic surgical care expenditures.RESULTS:The surgical care indicator results delineated the countries into four groups according to each country's income level. The cluster plot indicated that most high-income countries (for example, United States, United Kingdom, Norway) are in the first cluster. The second cluster consisted of four countries: Japan, San Marino, Marshall Islands, and Monaco. Low-income countries (for example, Ethiopia, Guatemala, Kenya) and middle-income countries (for example, Brazil, Turkey, Hungary) are represented in the third and fourth clusters, respectively. The third cluster had a high Silhouette index value (.75). The densities of both surgeons and medical imaging technology were risk determiners of catastrophic surgical care expenditures (Area Under Curve = .82).CONCLUSIONS:Our results demonstrate a need for more effective health plans if the differences between countries surgical care indicators are to be overcome. We recommend that health policymakers reconsider distribution strategies for the surgical workforce and medical imaging technology in the interest of accessibility and equality.

scholarly journals Silhouette index for determining optimal k-means clustering on images in different color models

2018 ◽  
Vol 7 (2.14) ◽  
pp. 105 ◽  
Author(s):  
Abd Rasid Mamat ◽  
Fatma Susilawati Mohamed ◽  
Mohamad Afendee Mohamed ◽  
Norkhairani Mohd Rawi ◽  
Mohd Isa Awang
Keyword(s):  
Image Processing ◽  
Color Model ◽  
Cluster Validation ◽  
Validation Process ◽  
Color Models ◽  
Silhouette Index ◽  
Hsv Color Model ◽  
Different Color ◽  
Lab Color Model ◽  
Cie Lab

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.  

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