Relevant bibliographies by topics / Jaccard coefficient

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Academic literature on the topic 'Jaccard coefficient'

Author: Grafiati
Published: 19 February 2023
Last updated: 15 June 2025

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Journal articles on the topic "Jaccard coefficient"

1

Chahal, Manoj. "Information Retrieval using Jaccard Similarity Coefficient." International Journal of Computer Trends and Technology 36, no. 3 (2016): 140–43. http://dx.doi.org/10.14445/22312803/ijctt-v36p124.

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Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.

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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.
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3

Zhang, YICHENG. "Design of Branch Predictor Based on Jaccard Coefficient." Journal of Physics: Conference Series 1487 (March 2020): 012003. http://dx.doi.org/10.1088/1742-6596/1487/1/012003.

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4

Lee, Soojung. "Improving Performance of Jaccard Coefficient for Collaborative Filtering." Journal of the Korea Society of Computer and Information 21, no. 11 (2016): 121–26. http://dx.doi.org/10.9708/jksci.2016.21.11.121.

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5

Taylor, Paul J., Ian J. Donald, Karen Jacques, and Stacey M. Conchie. "Jaccard's heel: Radex models of criminal behaviour are rarely falsifiable when derived using Jaccard coefficient." Legal and Criminological Psychology 17, no. 1 (2011): 41–58. http://dx.doi.org/10.1348/135532510x518371.

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6

Huynh, Hao Tuan, Nghia Duong-Trung, Dinh Quoc Truong, and Hiep Xuan Huynh. "Vietnamese Text Classification with TextRank and Jaccard Similarity Coefficient." Advances in Science, Technology and Engineering Systems Journal 5, no. 6 (2020): 363–69. http://dx.doi.org/10.25046/aj050644.

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7

Nugraheni, Murien. "Detection Coronavirus using Cased-Based Reasoning with Extended Jaccard Coefficient." IJISTECH (International Journal of Information System & Technology) 5, no. 1 (2021): 31. http://dx.doi.org/10.30645/ijistech.v5i1.112.

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Coronavirus Disease 2019 or known as COVID-19 is a new disease that can cause respiratory problems and pneumonia. This disease is caused by infection with Severe Acute Respiratory Syndrome Me Coronavirus 2 (SARS-CoV-2). Some of the clinical symptoms that appear vary, ranging from symptoms such as influenza, cough, cold, throat pain, muscle aches, headaches to those with serious complications such as pneumonia or sepsis. This research to build case-based reasoning for early detection of COVID-19 by looking at the characteristics of clinical symptoms seen in a person using the Extended Jaccard Coefficient method. The results show case-based reasoning for early detection of COVID-19 using the Extended Jaccard Coefficient method can model the level of similarity of a new case to an old case.
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8

Samanthula, Bharath K., and Wei Jiang. "Secure Multiset Intersection Cardinality and its Application to Jaccard Coefficient." IEEE Transactions on Dependable and Secure Computing 13, no. 5 (2016): 591–604. http://dx.doi.org/10.1109/tdsc.2015.2415482.

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9

Chaitra, Nada Filsa, Muhammad Ainul Yaqin, Rodhiyatus Saadah, and Riska Dwi Anggraeni. "Analisis dan Perancangan Software Pengukur Kemiripan Desain Database Relasional." ILKOMNIKA: Journal of Computer Science and Applied Informatics 2, no. 3 (2020): 229–39. http://dx.doi.org/10.28926/ilkomnika.v2i3.60.

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Pengukuran kemiripan entity relational diagram (ERD) dilakukan untuk mendapatkan nilai kemiripan secara semantik dan struktural pada dua ERD yang dibandingkan. Kemiripan struktural didapatkan dengan mendapatkan elemen-elemen yang ada pada ERD. kemiripan semantik dilakukan dengan membandingkan setiap kata pada data definition language (DDL) dari ERD. Untuk mengetahui nilai kesamaan tersebut pengecekan dilakukan dengan secara manual. Hal seperti ini membutuhkan waktu yang cukup lama. Selain itu, terjadinya human error juga sangat mungkin terjadi. Dalam penelitian ini penulis akan menganalisa dan merancang sebuah sistem yang dapat mengukur kemiripan ERD. Ada banyak metode yang digunakan untuk menganalisa kemiripan desain basis data relasional. Untuk kemiripan struktural menggunakan metode jaccard similarity, cosine coefficient, dice’s coefficient dan overlap coefficient. dan untuk kemiripan semantic digunakan levensthein. Hasil kemiripan ERD berupa nilai dari hasil perhitungan menggunakan metode jaccard similarity, cosine coefficient, dice’s coefficient, serta overlap coefficient untuk kemiripan struktural dan metode levensthein untuk kemiripan semantik
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10

Abdul Malek, Aminah, Nurhanani Abdul Rahim, Nor Farah Nabilah Mushtafa, Nadhirah Afiqah Zailan, and Norlyda Mohamed. "Multiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm." International Journal of Software Engineering and Computer Systems 7, no. 1 (2021): 1–11. http://dx.doi.org/10.15282/ijsecs.7.1.2021.1.0077.

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Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.
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