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Journal articles on the topic 'Nearest Neighbour'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mahfouz, Mohamed A. "INCORPORATING DENSITY IN K-NEAREST NEIGHBORS REGRESSION." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 144–49. http://dx.doi.org/10.26483/ijarcs.v14i3.6989.

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The application of the traditional k-nearest neighbours in regression analysis suffers from several difficulties when only a limited number of samples are available. In this paper, two decision models based on density are proposed. In order to reduce testing time, a k-nearest neighbours table (kNN-Table) is maintained to keep the neighbours of each object x along with their weighted Manhattan distance to x and a binary vector representing the increase or the decrease in each dimension compared to x’s values. In the first decision model, if the unseen sample having a distance to one of its neig

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2

Cunningham, Pádraig, and Sarah Jane Delany. "k-Nearest Neighbour Classifiers - A Tutorial." ACM Computing Surveys 54, no. 6 (2021): 1–25. http://dx.doi.org/10.1145/3459665.

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Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distan

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3

Stanley, Peter M. "Our Nearest Neighbour." Expository Times 96, no. 7 (1985): 209–10. http://dx.doi.org/10.1177/001452468509600706.

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Hasler, Caren, and Yves Tillé. "Balancedk-nearest neighbour imputation." Statistics 50, no. 6 (2016): 1310–31. http://dx.doi.org/10.1080/02331888.2016.1230615.

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Plataniotis, K. N., D. Androutsos, V. Sri, and A. N. Venetsanopoulos. "Nearest-neighbour multichannel filter." Electronics Letters 31, no. 22 (1995): 1910–11. http://dx.doi.org/10.1049/el:19951294.

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Chaplain, Gregory J., Dan Moore, Ian Hooper, Alastair Hibbins, John Sambles, and Timothy Starkey. "Beyond-nearest-neighbour metamaterials." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A156. http://dx.doi.org/10.1121/10.0023108.

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Engineering the dispersion of acoustic or elastic waves using coupling terms that spatially reach beyond the immediate local environment, or unit cell, is an “emerging topic” in Metamaterial design. In this talk, we present experimental studies in acoustic and elastic systems that realize beyond-nearest-neighbour coupling to introduce dispersion relations with extrema within the first Brilloun Zone. In acoustics, we use mixed waveguide-surfacewave coupling, while in elasticity we develop an elastic scaffold (made from meccano) with reconfigurable coupling elements and demonstrate the effects o

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Lu, Zhigang, and Hong Shen. "An accuracy-assured privacy-preserving recommender system for internet commerce." Computer Science and Information Systems 12, no. 4 (2015): 1307–26. http://dx.doi.org/10.2298/csis140725056l.

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Recommender systems, tool for predicting users? potential preferences by computing history data and users? interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attentions recently. The risk of revealing users? private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. The existing methods deploying

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Ati, Indri, and Ari Kusyanti. "Metode Ensemble Classifier untuk Mendeteksi Jenis Attention Deficit Hyperactivity Disorder (SDHD) pada Anak Usia Dini." Jurnal Teknologi Informasi dan Ilmu Komputer 6, no. 3 (2019): 301. http://dx.doi.org/10.25126/jtiik.2019631313.

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<p class="Abstract">Pada awal masa perkembangan, beberapa anak mengalami hambatan diantaranya sulit untuk diam, sulit untuk berkonsentrasi dan mengontrol perilakunya, apabila anak mengalami gangguan pemusatan perhatian dan sulit mengontrol perilaku yang sesuai, dapat disebut dengan ADHD (Attention Deficit Hyperactive Disorder). Ini merupakan masalah yang serius dikarenakan anak penyandang ADHD mengalami masalah perilaku sosial, emosional dan mengalami kesulitan belajar sekolah sehingga akan mempengaruhi perkembangan pada masa dewasa anak penyandang ADHD. Oleh karena itu perlu diketahui g

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Myasnikov, E. "Exact Nearest Neighbour Search within Constrained Neighbourhood Using the Forest of Vp-Tree-Like Structures." Journal of Physics: Conference Series 2096, no. 1 (2021): 012199. http://dx.doi.org/10.1088/1742-6596/2096/1/012199.

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Abstract In this paper, we address the problem of fast nearest neighbour search. Unfortunately, well-known indexing data structures, such as vp-trees perform poorly on some datasets and do not provide significant acceleration compared to the brute force approach. In the paper, we consider an alternative solution, which can be applied if we are not interested in some fraction of distant nearest neighbours. This solution is based on building the forest of vp-tree-like structures and guarantees the exact nearest neighbour search in the epsilon-neighbourhood of the query point.

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Wang, Zizhu, and Miguel Navascués. "Two-dimensional translation-invariant probability distributions: approximations, characterizations and no-go theorems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2217 (2018): 20170822. http://dx.doi.org/10.1098/rspa.2017.0822.

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We study the properties of the set of marginal distributions of infinite translation-invariant systems in the two-dimensional square lattice. In cases where the local variables can only take a small number d of possible values, we completely solve the marginal or membership problem for nearest-neighbours distributions ( d = 2, 3) and nearest and next-to-nearest neighbours distributions ( d = 2). Remarkably, all these sets form convex polytopes in probability space. This allows us to devise an algorithm to compute the minimum energy per site of any TI Hamiltonian in these scenarios exactly. We

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11

Wade, Andrew R. "Explicit laws of large numbers for random nearest-neighbour-type graphs." Advances in Applied Probability 39, no. 2 (2007): 326–42. http://dx.doi.org/10.1239/aap/1183667613.

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Under the unifying umbrella of a general result of Penrose and Yukich (Annals of Applied Probability13 (2003), 277-303) we give laws of large numbers (in the Lp sense) for the total power-weighted length of several nearest-neighbour-type graphs on random point sets in ℝd, d ∈ ℕ. Some of these results are known; some are new. We give limiting constants explicitly, where previously they have been evaluated in less generality or not at all. The graphs we consider include the k-nearest-neighbours graph, the Gabriel graph, the minimal directed spanning forest, and the on-line nearest-neighbour grap

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Wade, Andrew R. "Explicit laws of large numbers for random nearest-neighbour-type graphs." Advances in Applied Probability 39, no. 02 (2007): 326–42. http://dx.doi.org/10.1017/s0001867800001786.

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Under the unifying umbrella of a general result of Penrose and Yukich (Annals of Applied Probability 13 (2003), 277-303) we give laws of large numbers (in the L p sense) for the total power-weighted length of several nearest-neighbour-type graphs on random point sets in ℝ d , d ∈ ℕ. Some of these results are known; some are new. We give limiting constants explicitly, where previously they have been evaluated in less generality or not at all. The graphs we consider include the k-nearest-neighbours graph, the Gabriel graph, the minimal directed spanning forest, and the on-line nearest-neighbour

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McCulloch, D. G., D. R. McKenzie, D. J. H. Cockayne, and C. M. Goringe. "Experimental and Theoretical Characterisation of Structure in Thin Disordered Films." Microscopy and Microanalysis 4, S2 (1998): 704–5. http://dx.doi.org/10.1017/s1431927600023643.

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The electron microscope provides an ideal environment for the structural analysis of small volumes of amorphous and polycrystalline materials by collecting scattering information as a function of energy loss and momentum transfer. The scattering intensity at zero energy loss can be readily processed to a reduced density function G(r), providing information on nearest neighbour distances and bond angles[l]. Figure 1(a) shows the G(r) for glassy carbon, a turbostratic form of graphite. The three nearest neighbours in glassy carbon (labelled 1-3 in figure 1) are at 1.42 Å, 2.44 Å and 3.75 Å respe

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14

S Sao, Sphurti, and Rahila Shiekh. "A Review: Fast nearest Neighbour Search with Keywords." International Journal of Scientific Engineering and Research 4, no. 1 (2016): 52–54. https://doi.org/10.70729/ijser15657.

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15

Ferdiani Harahap, Rio, and Sawaluddin. "Study vehicle routing problem using Nearest Neighbor Algorithm." Journal of Physics: Conference Series 2421, no. 1 (2023): 012027. http://dx.doi.org/10.1088/1742-6596/2421/1/012027.

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Abstract Vehicle routing problem (VRP) has a key role in logistics management. VRP plays a role in designing the optimal route used by a number of vehicles placed at the depot to serve a number of customers with known requests. To solve the VRP, the nearest neighbor algorithm used to get the most optimal results. The Nearest Neighbor algorithm is a heuristic method which is done by starting the starting point than looking for the nearest point. In this paper, algorithm nearest neighbour can save the distance of 13,14% and cost 13,17%.

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Kraus, Johann M., Ludwig Lausser, and Hans A. Kestler. "Exhaustivek-nearest-neighbour subspace clustering." Journal of Statistical Computation and Simulation 85, no. 1 (2014): 30–46. http://dx.doi.org/10.1080/00949655.2014.933222.

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17

Simões, C. "Nearest Neighbour Interactions and Symmetries." Journal of Physics: Conference Series 335 (December 28, 2011): 012031. http://dx.doi.org/10.1088/1742-6596/335/1/012031.

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18

Samsudin, Noor A., and Andrew P. Bradley. "Nearest neighbour group-based classification." Pattern Recognition 43, no. 10 (2010): 3458–67. http://dx.doi.org/10.1016/j.patcog.2010.05.010.

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19

Qu, Yanpeng, Changjing Shang, Neil Mac Parthaláin, Wei Wu, and Qiang Shen. "Multi-functional nearest-neighbour classification." Soft Computing 22, no. 8 (2017): 2717–30. http://dx.doi.org/10.1007/s00500-017-2528-4.

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20

Samworth, Richard J. "Optimal weighted nearest neighbour classifiers." Annals of Statistics 40, no. 5 (2012): 2733–63. http://dx.doi.org/10.1214/12-aos1049.

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21

Fakhrurrifqi, Muhammad, and Retantyo Wardoyo. "Perbandingan Algoritma Nearest Neighbour, C4.5 dan LVQ untuk Klasifikasi Kemampuan Mahasiswa." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 2 (2013): 145. http://dx.doi.org/10.22146/ijccs.3353.

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AbstrakPada pelaksanaan acara perkuliahan atau saat proses balajar mengajar, dosen sering terkendala dengan kemampuan mahasiswa pada suatu matakuliah di satu kelas yang tidak merata. Oleh karena itu dosen terlebih dulu mengetahui kemampuan setiap mahasiswanya dengan salah satu caranya adalah dengan melihat karakteristik setiap mahasiswa dan kemudian dibandingkan dengan mahasiswa-mahasiswa sebelumnya dalam menyelesaian suatu mata kuliah.Pada penelitian ini, akan dilakukan perbandingan tingkat akurasi antara tiga algoritma, yaitu : Nearest Neighbour, C45 dan LVQ, pada kasus klasifikasi kemampuan

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22

Sletvold, Nina, and Geir Hestmark. "A comparative test of the predictive power of neighbourhood models in natural populations of Lasallia pustulata." Canadian Journal of Botany 77, no. 11 (2000): 1655–61. http://dx.doi.org/10.1139/b99-112.

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Three different neighbourhood models were tested to predict individual performance in 50 natural populations of the saxicolous lichen Lasallia pustulata (L.) Mérat. Mean distance to neighbours was clearly the best predictor, accounting for most of the variation in 70% of the populations. In contrast a model based on the number of neighbours within a circle of fixed radius usually had the lowest predictive power. Polygon areas generated by Dirichlet tessellation had a predictive power slightly less than the nearest neighbour approach. The predictive power of all three neighbourhood models was s

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23

Sanggala, Ekra, and Muhammad Ardhya Bisma. "Perbandingan Savings Algorithm dengan Nearest Neighbour dalam Menyelesaikan Russian TSP Instances." Jurnal Media Teknik dan Sistem Industri 7, no. 1 (2023): 27. http://dx.doi.org/10.35194/jmtsi.v7i1.3039.

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Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. Several heuristics are popular for solving TSP, for example Savings Algorithm and Nearest Neighbour. Performance heuristics on solving TSP are diverse, so there is need of reference for choosing a heuristic. Comparing heuristics on solving instance can be a reference for choosing a heuristic. This paper will discuss about comparison Savings Algorithm and Nearest Neighbour on Solving Russian TSP Instances. For gene

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24

Rezky, Syarifah Fadillah, Devri Suherdi, Rita Hamdani, Trinanda Syahputra, and Amrullah Amrullah. "Perbandingan Implementasi Interpolasi Nearest Neighbour Dan Bilinier Dalam Scaling Pada File Bitmap." J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD) 6, no. 2 (2023): 604. http://dx.doi.org/10.53513/jsk.v6i2.8676.

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Di dalam penerapan transformasi spasial pada pembesaran citra seringkali terjadi situasi diperoleh koordinat titik hasil maupun titik asal yang bernilai tidak integer, padahal koordinat titik di dalam pembesaran citra harus selalu bernilai integer, jika demikian maka diperlukan interpolasi untuk menentukan nilai keabuan pada koordinat titik hasil berdasarkan nilai keabuan pada koordinat titik asal. Pada penelitian ini diimplementasikan dua interpolasi, yaitu interpolasi bilinier dan nearest neighbour. Hasil pengujian menunjukkan bahwa kelebihan dari interpolasi nearet neighbour adalah kemudaha

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25

Myshlyavtsev, A. V., and V. P. Zhdanov. "The effect of nearest-neighbour and next-nearest-neighbour lateral interactions on thermal desorption spectra." Chemical Physics Letters 162, no. 1-2 (1989): 43–46. http://dx.doi.org/10.1016/0009-2614(89)85063-8.

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26

Wisudawati, Nidya, Ananda Valentine, and Rurry Patradhiani. "Usulan Perbaikan Rute Pengangkutan Sampah Menggunakan Metode Branch And Bound Dan Nearest Neighbour Untuk Meminimalkan Biaya Transportasi." Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri 8, no. 2 (2022): 288. http://dx.doi.org/10.24014/jti.v8i2.20100.

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Penelitian ini bertujuan untuk memilih rute optimal pengangkutan sampah pada UKM Kelompok Usaha Bersama Peduli Sampah Kecamatan Tanah Abang sehingga dapat meminimalkan biaya transportasi. Metode Branch and Bound dan Nearest Neighbour dipilih dalam menyelesaikan masalah pada penelitian ini. Metode Branch and Bound adalah metode pencarian di dalam ruang solusi secara sistematis. Sedangkan metode Nearest Neighbour adalah sebuah teknik menyelesaikan permasalahan rute dengan menentukan titik terdekat dengan jarak terpendek. Hasil perhitungan untuk rute awal pengangkutan sampah didapatkan jarak temp

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Baddeley, A. J., M. N. M. Van Lieshout, and J. Møller. "Markov properties of cluster processes." Advances in Applied Probability 28, no. 2 (1996): 346–55. http://dx.doi.org/10.2307/1428060.

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We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they

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Baddeley, A. J., M. N. M. Van Lieshout, and J. Møller. "Markov properties of cluster processes." Advances in Applied Probability 28, no. 02 (1996): 346–55. http://dx.doi.org/10.1017/s0001867800048503.

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We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they

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Tursina, Tursina, and Rina Septiriana. "Comparison of the nearest neighbor algorithm and C4.5 for the retrieval on case-based reasoning process (case study: children respiratory disorders)." Jurnal Mantik 8, no. 1 (2024): 174–85. http://dx.doi.org/10.35335/mantik.v8i1.5018.

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The diagnosis of respiratory problems was usually made through direct consultation with a pediatric respiratory specialist or by studying several previous respiratory disorders cases. These cases were gleaned from prior experiences or the knowledge of subject-matter experts. Case-Based Reasoning (CBR) is the processing of diagnosing a patient based on past cases or expertise. Retrieve, reuse, revise, and retain are some of the steps of case-based reasoning. The retrieval stage of CBR was where the classification method searches for similarity values. Numerous algorithms exist for classificatio

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Mehta, Sumet, Xiangjun Shen, Jiangping Gou, and Dejiao Niu. "A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance." Information 9, no. 9 (2018): 234. http://dx.doi.org/10.3390/info9090234.

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The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each cl

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31

Zhao, Ning, Jingyue Xu, and Gang Zhou. "Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN." Journal of Physics: Conference Series 2636, no. 1 (2023): 012046. http://dx.doi.org/10.1088/1742-6596/2636/1/012046.

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Abstract Fan plays the roles of the forced draft fan, induced draft fan, primary air fan, seal fan, and powder exhauster. As an important auxiliary of the fossil-fuel power station, its working environment is harsh. Timely and efficient fault diagnosis can effectively reduce equipment failure and shutdown losses, and improve the efficiency of thermal power generation. K-Nearest Neighbour (KNN) has good classification ability for non-stationary data samples. In response to the shortcomings of the traditional KNN algorithm, this paper constructs a fault diagnosis model based on the voting weight

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Ilmi, Nurul, and Hertanto Suryoprayogo. "Pengenalan Angka Bahasa Isyarat dengan Menggunakan Local Directional Pattern dan Klasifikasi K-Nearest Neighbour." Journal of Informatics and Communication Technology (JICT) 4, no. 1 (2022): 48–55. http://dx.doi.org/10.52661/j_ict.v4i1.103.

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Dengan semakin berkembangnya teknologi, diharapkan System Pengenalan Bahasa Isyarat juga semakin berkembang. Sistem Pengenalan Angka Bahasa Isyarat ini dapat dilakukan dengan pembelajaran mesin. Pada penelitian ini dijelaskan tentang sistem pengenalan citra angka Bahasa isyarat dengan menggunakan metode ekstraksi ciri Local Directional Pattern dan Klasifikasi K-Nearest Neighbour. Sistem ini terdiri dari beberapa tahap, yaitu pengumpulan data, pre-processing, ekstraksi ciri dan klasifikasi. Pengujian system ini menggunakan data Turkey Ankara Ayrancı Anadolu High School's Sign Language Digits Da

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N, MINOJINI, GAYATHRI R. KRISHNA, REKHA A, and SOWMIYAA P. "Dynamic Nearest Neighbour Search With Keywords." IJARCCE 4, no. 3 (2015): 580–82. http://dx.doi.org/10.17148/ijarcce.2015.43139.

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Hall, Peter, and Richard J. Samworth. "Properties of bagged nearest neighbour classifiers." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, no. 3 (2005): 363–79. http://dx.doi.org/10.1111/j.1467-9868.2005.00506.x.

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Irel, A. "On sequential nearest neighbour regression estimation." Sequential Analysis 18, no. 1 (1999): 33–42. http://dx.doi.org/10.1080/07474949908836419.

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Carravetta, Francesco. "Nearest-neighbour modelling of reciprocal chains." Stochastics 80, no. 6 (2008): 525–84. http://dx.doi.org/10.1080/17442500802088517.

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Fasshauer, E. "Non-nearest neighbour ICD in clusters." New Journal of Physics 18, no. 4 (2016): 043028. http://dx.doi.org/10.1088/1367-2630/18/4/043028.

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Bermejo, Sergio, and Joan Cabestany. "Adaptive soft k-nearest-neighbour classifiers." Pattern Recognition 33, no. 12 (2000): 1999–2005. http://dx.doi.org/10.1016/s0031-3203(99)00186-7.

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39

Ma, Hongxing, Jianping Gou, and Xili Wang. "A pseudo nearest centroid neighbour classifier." International Journal of Computational Science and Engineering 17, no. 1 (2018): 55. http://dx.doi.org/10.1504/ijcse.2018.094420.

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Wang, Xili, Jianping Gou, and Hongxing Ma. "A pseudo nearest centroid neighbour classifier." International Journal of Computational Science and Engineering 17, no. 1 (2018): 55. http://dx.doi.org/10.1504/ijcse.2018.10015540.

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Roy, A., and N. V. Madhusudana. "A chiral axial next nearest neighbour." European Physical Journal E 1, no. 4 (2000): 319. http://dx.doi.org/10.1007/s101890050032.

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Cuninghame-Green, R. A., and G. Harries. "Nearest-neighbour rules for emergency services." Zeitschrift für Operations Research 32, no. 5 (1988): 299–306. http://dx.doi.org/10.1007/bf01920299.

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Bouten, M., and C. van den Broeck. "Nearest-Neighbour Classifier for the Perceptron." Europhysics Letters (EPL) 26, no. 1 (1994): 69–74. http://dx.doi.org/10.1209/0295-5075/26/1/012.

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Luk, Andrew, and James E. S. Macleod. "An alternative nearest neighbour classification scheme." Pattern Recognition Letters 4, no. 5 (1986): 375–81. http://dx.doi.org/10.1016/0167-8655(86)90059-0.

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45

Dahl, Fredrik A. "Convergence of random -nearest-neighbour imputation." Computational Statistics & Data Analysis 51, no. 12 (2007): 5913–17. http://dx.doi.org/10.1016/j.csda.2006.11.007.

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46

Laaksonen, Seppo. "Regression-based nearest neighbour hot decking." Computational Statistics 15, no. 1 (2000): 65–71. http://dx.doi.org/10.1007/s001800050037.

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47

Walters, Mark. "Small components in -nearest neighbour graphs." Discrete Applied Mathematics 160, no. 13-14 (2012): 2037–47. http://dx.doi.org/10.1016/j.dam.2012.03.033.

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Deti Fusvita, Asnawati, Feri Hari Utami. "KNN (K-NEAREST NEIGHBOUR) PENERAPAN ALGORITMA KNN (K-NEAREST NEIGHBOUR)DALAM KLASIFIKASI DATA PINJAMAN ANGGOTA KOPERASI." Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Lubuklinggau 3, no. 1 (2021): 1–5. http://dx.doi.org/10.52303/jb.v3i1.32.

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Koperasi Pegawai Negeri sudah menggunakan komputer sebagai sarana atau media dalam pendukung dalam pembuatan laporan dengan menggunakan aplikasi Microsoft Word dan Microsoft Excel, sehingga masih banyak ditemukan kekurangan-kekurangan dalam hal pelayanan pinjaman pada anggota koperasi. Aplikasi klasifikasi data pinjaman anggota Koperasi Pegawai Negeri Guru-guru SD Kecamatan Seluma dibuat menggunakan bahasa pemrograman Visual Basic .Net sebagai media pembuatan aplikasi dan database SQL Server 2008 sebagai media penyimpanan hasil pengolahan data. Dalam proses klasifikasi data pinjaman anggota ko

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Ismail, G., and S. Hassan. "Metastability of Ising spin chains with nearest-neighbour and next-nearest-neighbour interactions in random fields." Chinese Physics 11, no. 9 (2002): 948–54. http://dx.doi.org/10.1088/1009-1963/11/9/318.

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Ismail, G., K. Bannora, and S. Hassan. "Dynamics of Ising spin chains with nearest-neighbour and next-nearest-neighbour interactions in random fields." physica status solidi (b) 241, no. 15 (2004): 3607–23. http://dx.doi.org/10.1002/pssb.200402083.

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