A Cluster Analysis with Complete Linkage and Ward's Method for Health Service Data in Makassar City

Muthahharah, Isma; Juhari, Agusalim

doi:10.30812/varian.v4i2.883

Cited by 9 publications

(12 citation statements)

References 4 publications

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“…The hierarchical Ward’s method was applied in the first stage of the cluster analysis [ 53 ]. It aims to obtain clusters with cases (culling categories) that are as similar as possible to each other and as different as possible from cases (culling categories) belonging to other clusters [ 54 , 55 ]. This can be obtained by merging all possible cluster pairs and selecting, each time, the cluster with the minimum sum of squared deviations [ 56 , 57 , 58 , 59 ] using an approach based on the analysis of variance to determine the distance between clusters [ 55 , 60 , 61 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

“…It aims to obtain clusters with cases (culling categories) that are as similar as possible to each other and as different as possible from cases (culling categories) belonging to other clusters [ 54 , 55 ]. This can be obtained by merging all possible cluster pairs and selecting, each time, the cluster with the minimum sum of squared deviations [ 56 , 57 , 58 , 59 ] using an approach based on the analysis of variance to determine the distance between clusters [ 55 , 60 , 61 , 62 , 63 ]. The measure of the distance between cases (culling categories) and the mean value of a given cluster was the error sum of squares ( EES ), given by the following formula [ 64 , 65 , 66 ]:

where x i is the value of the variable that is a clustering criterion for the i th case, k is the number of cases (culling categories) within the cluster,

is the mean value of this variable within the cluster.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Grzesiak

Adamczyk

Zaborski

et al. 2022

Animals

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The aims of the study were: (i) to compare survival curves for cows culled for different reasons over three successive lactations using the Kaplan–Meier estimator; (ii) to determine the effects of breeding documentation parameters on cow survival; (iii) to investigate the similarity between culling categories. The survival times for a subset of 347,939 Holstein-Friesian cows culled between 2017 and 2018 in Poland were expressed in months from calving to culling or the end of lactation. The survival tables were constructed for each culling category and lactation number. The survival curves were also compared. The main culling categories were reproductive disorders—40%, udder diseases—13 to 15%, and locomotor system diseases—above 10%. The survival curves for cows from individual culling categories had similar shapes. A low probability of survival curves for metabolic and digestive system diseases and respiratory diseases was observed in each of the three lactations. The contagious disease category was almost non-existent in the first lactation. The greatest influence on the relative culling risk was exerted by age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required.

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Section: Methodsmentioning

confidence: 99%

where x i is the value of the variable that is a clustering criterion for the i th case, k is the number of cases (culling categories) within the cluster,

is the mean value of this variable within the cluster.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Grzesiak

Adamczyk

Zaborski

et al. 2022

Animals

View full text Add to dashboard Cite

show abstract

“…ere were four types of medical services, namely, hospitals, health centers, home care, and telemedicine, with 15 divisions [7]. ese studies can analyze the relevant conditions, but the research on the accuracy rate needs to be improved.…”

Section: Related Workmentioning

confidence: 99%

“…In constructing a user interest model, a combination of two methods can be adopted. rough the above analysis, the data sources of web page information collection mainly include the following aspects: (1) user browsing behavior, (2) web page information browsed, (3) user visit times, (4) user residence time on the web page, (5) keywords, (6) server log information, (7) data and pages saved and downloaded by the user, and (8) other information input by the user.…”

Section: User Interests Based On Big Data Clustermentioning

confidence: 99%

Application of Multimedia Technology in Online Piano Teaching

Ding

Huang

2022

Mobile Information Systems

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Multimedia teaching has the characteristics of flexibility, nonlinear structure, and the combination of multiple senses, which can fully mobilize the enthusiasm and initiative of students. Because the traditional piano teaching method is too old-fashioned and single, which is not conducive to stimulating students’ interest in learning, the piano teaching is studied according to the methods of information technology, online education, and mixed teaching. This paper controls the rhythm of teaching through multimedia teaching technology, stimulates students’ thirst for knowledge, and uses multimedia technology to enrich piano teaching resources to meet teaching needs. Then, by means of a questionnaire, the attitude of most of the high-efficiency students towards online piano teaching with multimedia technology is understood. At the same time, the cluster analysis system is used to analyze the online public opinion evaluation of the three teaching methods by collecting the network behavior of users in various distribution platforms on the Internet and analyzing the feature vector according to the function space formed by the relevant evaluation indicators. The accuracy of the system is above 82%, so the teaching evaluation is effective. Through the value evaluation model of the system, user dynamics can be accurately understood and applied to other fields.

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“…Prosedur Pengelompokkan sama seperti dengan single linkage, perbedaannya terletak pada perhitungan jarak antar kelompok. 𝑑 (𝑥𝑦)𝑧 = max{𝑑 𝑥𝑧 , 𝑑 𝑦𝑧 } ..................................................................................................................... (6) 3.di mana 𝑛 (𝑥𝑦) adalah banyaknya anggota yang bergabung kelompok (𝑥𝑦) dan 𝑛 𝑥 , 𝑛 𝑦 , 𝑛 𝑧 adalah banyaknya anggota berturut-turut yang bergabung dalam kelompok 𝑥, 𝑦 dan 𝑧(Muthahharah & Juhari, 2021).…”

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Aplikasi Pendekatan Agglomerative Hierarchical Time Series Clustering untuk Peramalan Data Harga Minyak Goreng di Indonesia

Zen¹,

Wahyuningsih²,

Dani³

2022

semnasoffstat

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Minyak goreng merupakan komoditas pokok pangan strategis di Indonesia. Tingginya konsumsi minyak goreng setiap tahunnya diiringi dengan meningkatnya permintaan dari pasar sehingga menyebabkan harga minyak goreng meningkat tiap tahunnya. Tujuan penelitian ini adalah untuk menggerombolkan Provinsi di Indonesia berdasarkan pola runtun waktu harga minyak goreng dan mengevaluasi peramalan tingkat kelompok. Algoritma Pengelompokan yang digunakan adalah Agglomerative Hierarcical Clustering (AHC) dengan ukuran kemiripan dynamic time warping (DTW), autocorrelation function (ACF), dan euclidean distance. Algoritma dan jumlah cluster optimal dipilih berdasarkan koefisien korelasi cophenetic dan koefisien silhouette. Kemudian, setiap cluster yang terbentuk akan dilakukan pemodelan Autoregressive Integrated Moving Average (ARIMA) dengan pendekatan Auto ARIMA. Evaluasi peramalan menggunakan Mean Absolute Percentage Error (MAPE). Penelitian ini menunjukkan hasil algoritma optimal yang terpilih adalah average linkage dengan ukuran kemiripan euclidean distance. Nilai koefisien korelasi cophentic dan koefisien silhouette berturut-turut diperoleh sebesar 0,8281 dan 0,4296. Nilai MAPE yang diperoleh berturut-turut sebesar 0,3399 dan 0,0793. Minyak goreng merupakan komoditas pokok pangan strategis di Indonesia. Tingginya konsumsi minyak goreng setiap tahunnya diiringi dengan meningkatnya permintaan dari pasar sehingga menyebabkan harga minyak goreng meningkat tiap tahunnya. Tujuan penelitian ini adalah untuk menggerombolkan Provinsi di Indonesia berdasarkan pola runtun waktu harga minyak goreng dan mengevaluasi peramalan tingkat kelompok. Algoritma Pengelompokan yang digunakan adalah Agglomerative Hierarcical Clustering (AHC) dengan ukuran kemiripan dynamic time warping (DTW), autocorrelation function (ACF), dan euclidean distance. Algoritma dan jumlah cluster optimal dipilih berdasarkan koefisien korelasi cophenetic dan koefisien silhouette. Kemudian, setiap cluster yang terbentuk akan dilakukan pemodelan Autoregressive Integrated Moving Average (ARIMA) dengan pendekatan Auto ARIMA. Evaluasi peramalan menggunakan Mean Absolute Percentage Error (MAPE). Penelitian ini menunjukkan hasil algoritma optimal yang terpilih adalah average linkage dengan ukuran kemiripan euclidean distance. Nilai koefisien korelasi cophentic dan koefisien silhouette berturut-turut diperoleh sebesar 0,8281 dan 0,4296. Nilai MAPE yang diperoleh berturut-turut sebesar 0,3399 dan 0,0793.

show abstract

A Cluster Analysis with Complete Linkage and Ward's Method for Health Service Data in Makassar City

Cited by 9 publications

References 4 publications

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Application of Multimedia Technology in Online Piano Teaching

Aplikasi Pendekatan Agglomerative Hierarchical Time Series Clustering untuk Peramalan Data Harga Minyak Goreng di Indonesia

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