The optimized K-means algorithms for improving randomly-initialed midpoints

Shi, Guojun; Gao, Bingkun; Zhang, Li

doi:10.1109/mic.2013.6758177

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Then, the specimens have been subjected to two step sintering process in which they were heated in air in electric furnace to 100 °C for 24 hrs., and then they were exposed to 950 °C for 3 hrs., as shown in Fig. 1 (d) [12][13][14]. Fig.…”

Section: Specimens Manufacturing and Porosity Measurement III Mamentioning

confidence: 99%

“…Iron oxide (Fe 3 O 4 ) is material used for corrosion resistance purposes. This material was selected as a model material for a high porosity material through which the relationship between the resulting porosity and its physical properties could be linked [12]. Very little has been published about this particular material regarding the measurement of porosity and predicting their behavior using the computer vision analysis.…”

Section: Introductionmentioning

confidence: 99%

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Porosity Measurement of Iron Oxide by Using Computer Vision System

El-Nasr¹,

Saleh²,

Alshennawy³

2020

International Journal of Engineering Research and Technology

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In this work, a proposed Computer Vision System (CVS) has been applied to measure the porosity of iron oxide (Fe 3 O 4) specimens. Measurement of the porosity using CVS reduces time, effort and cost compared to the conventional methods. Fe 3 O 4 was received from iron rolling process in the form of thin layer, and then converted to powder by grinding process and filtering by 200 μm sieve. Fe 3 O 4 specimens were then manufactured by powder metallurgy (PM) technique using steel die subjected to a compression load (300 KN) to form cylindrical discs with 27 mm diameter and 10 mm height. Optical microscope with digital camera has been used in capturing images of the surfaces of Fe 3 O 4 specimens. The proposed CVS uses k-means algorithms, which divide the intended surfaces into distinct regions and by computing the ratio between these regions, the specimen porosity is obtained. Layers of the specimens are removed to measure the average porosity inside the specimens. The average value of porosity of specimens has been obtained for the original specimen and its mean value is ∼26.39%. In comparison, the porosity computed by CVS was very close to that obtained using conventional method, which confirms the efficiency of this technique.

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Section: Specimens Manufacturing and Porosity Measurement III Mamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Porosity Measurement of Iron Oxide by Using Computer Vision System

El-Nasr¹,

Saleh²,

Alshennawy³

2020

International Journal of Engineering Research and Technology

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“…Among the techniques employed in data mining, clustering is a prominent one. Scientific clustering in data mining involves grouping a set of data or objects into clusters, where each cluster contains similar data while being distinct from others [10]. The algorithm has gained significant attraction across various industries.…”

Section: K-means Clusteringmentioning

confidence: 99%

Optimizing Patient Medical Records Grouping through Data Mining and K-Means Clustering Algorithm: A Case Study at RSUD Mohammad Natsir Solok

Dony Novaliendry,

Tegar Wibowo,

Noper Ardi

et al. 2023

Int. J. Onl. Eng.

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RSUD Mohammad Natsir Solok, located in Solok City, provides comprehensive individual health services within its premises, offering both inpatient and outpatient care with 24-hour service availability. Inpatient services encompass emergency care and basic health services. A crucial component of healthcare operations is medical records, which consist of documented information pertaining to patient identity, examinations, treatments, procedures, and other services rendered. Medical records are essential and should be meticulously created in written or electronic form to ensure completeness and clarity. One common challenge encountered in maintaining medical records is the presence of overlapping data. To tackle this issue, data mining techniques are employed, with clustering being the primary method of choice. The K-Means algorithm is specifically utilized for clusterization purposes. By applying this data mining process and grouping patient medical records, valuable insights into the patterns of disease spread across different villages can be obtained. After applying K-Means clustering method, four distinct clusters were identified. The first cluster comprises 562 items, the second has 406 items, and the third and fourth have 791 and 279 items, respectively. These findings can serve as a reference for the local government, particularly the Solok City Health Office, to facilitate disease prevention initiatives and awareness campaigns. Decision-making related to disease sources, diagnosis, age, and gender of the affected patient can be informed by this data analysis.

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“…Tujuan dari dilakukannya penelitian ini adalah untuk memberikan solusi terhadap permasalahan dalam proses penentuan dan pemilihan nilai initial centroid yang masih dilakukan secara acak pada Algoritma K-means konvensional [10] [11]. Dalam penelitian ini, penentuan dan pemilihan nilai initial centroid dilakukan menggunakan metode iteratif.…”

Section: Pendahuluanunclassified

Pendekatan Initial Centroid Search Untuk Meningkatkan Efisiensi Iterasi Klustering K-Means

Nasution¹,

Hasibuan²

2020

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Pengelompokan K-Means bertujuan untuk mengumpulkan satu set titik pusat cluster yang optimal melalui iterasi yang berurutan. Fakta bahwa semakin optimal posisi dari titik pusat awal maka semakin sedikit jumlah iterasi dari algoritma pengelompokkan K-Means untuk konvergen. Oleh karena itu, Salah satu cara untuk menemukan set initial centroid adalah melalui metode iteratif guna mencari sejumlah initial centroid yang lebih baik untuk proses pengelompokan K-Means. Langkah awal yang kami lakukan adalah mengambil sampel data dari set data dan menjalankan algoritma K-Means sebagai proses awal untuk inisialisasi centroid cluster. Kemudian kami mengulang proses iterasi dengan sejumlah initial centroid yang telah diinisialisasikan sebelumnya dan mengukur hasil pengelompokkan melalui sum-of-square-error guna menentukan kebaikan keanggotaan cluster. Centroid akhir yang memberikan jarak terendah yang akan kami teruskan ke proses pengelompokan K-means secara lengkap. Harapan kami adalah pendekatan ini akan mengarah pada set initial centroid yang lebih baik sebagai proses pengelompokan K-Means sehingga mampu meningkatkan kinerja Algoritma K-Means karena hasil konvergensi Algoritma K-Means akan berbanding lurus dengan pemilihan initial centroid.

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The optimized K-means algorithms for improving randomly-initialed midpoints

Cited by 7 publications

References 2 publications

Porosity Measurement of Iron Oxide by Using Computer Vision System

Porosity Measurement of Iron Oxide by Using Computer Vision System

Optimizing Patient Medical Records Grouping through Data Mining and K-Means Clustering Algorithm: A Case Study at RSUD Mohammad Natsir Solok

Pendekatan Initial Centroid Search Untuk Meningkatkan Efisiensi Iterasi Klustering K-Means

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