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{"work":{"id":45421853,"created_at":"2021-03-07T22:51:04.577-08:00","from_world_paper_id":null,"updated_at":"2024-12-02T22:41:47.150-08:00","_data":{"ai_abstract":"Clustering is an unsupervised classification technique that groups data points based on their proximity, often using distance metrics such as Euclidean distance. The K-means algorithm iteratively assigns observations to clusters, minimizing within-cluster scatter and resulting in a number of clusters determined by the user. Variants like K-medoids cater to categorical data and pairwise distances, and the choice of the number of clusters can be guided by methods such as gap statistics.","publication_date":"2021,,"},"document_type":"teaching_document","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"Clustering","broadcastable":false,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [2363014]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":65935055,"attachmentType":"pdf"}"><img alt="First page of “Clustering”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/65935055/mini_magick20210307-20290-91rse5.png?1615188110" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Clustering</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="2363014" href="https://uni-mysore.academia.edu/ALIMOULAEINEJAD"><img alt="Profile image of ALI MOULAEI NEJAD" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/2363014/744683/18272526/s65_ali.moulaei_nejad.jpg" />ALI MOULAEI NEJAD</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2021</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">33 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 45421853; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=45421853"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--detail ds2-5-body-md">AI-generated Abstract</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Clustering is an unsupervised classification technique that groups data points based on their proximity, often using distance metrics such as Euclidean distance. The K-means algorithm iteratively assigns observations to clusters, minimizing within-cluster scatter and resulting in a number of clusters determined by the user. Variants like K-medoids cater to categorical data and pairwise distances, and the choice of the number of clusters can be guided by methods such as gap statistics.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":65935055,"attachmentType":"pdf","workUrl":"https://www.academia.edu/45421853/Clustering"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":65935055,"attachmentType":"pdf","workUrl":"https://www.academia.edu/45421853/Clustering"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="65935055" data-landing_url="https://www.academia.edu/45421853/Clustering" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="74679895" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/74679895/Improvement_of_traditional_k_means_algorithm_through_the_regulation_of_distance_metric_parameters">Improvement of traditional k-means algorithm through the regulation of distance metric parameters</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2662496" href="https://vtu.academia.edu/SuhasABhyratae">Suhas A Bhyratae</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2013 7th International Conference on Intelligent Systems and Control (ISCO), 2013</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improvement of traditional k-means algorithm through the regulation of distance metric parameters","attachmentId":82744606,"attachmentType":"pdf","work_url":"https://www.academia.edu/74679895/Improvement_of_traditional_k_means_algorithm_through_the_regulation_of_distance_metric_parameters","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/74679895/Improvement_of_traditional_k_means_algorithm_through_the_regulation_of_distance_metric_parameters"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="33893396" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/33893396/Clustering_and_the_k_means_Algorithm">Clustering and the k-means Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="66460686" href="https://independent.academia.edu/amyxu20">amy xu</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Clustering and the k-means Algorithm","attachmentId":53866977,"attachmentType":"pdf","work_url":"https://www.academia.edu/33893396/Clustering_and_the_k_means_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/33893396/Clustering_and_the_k_means_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="3807384" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3807384/Means_CLUSTERING_ALGORITHM">Means CLUSTERING ALGORITHM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3194628" href="https://iaeme.academia.edu/iaeme">iaeme iaeme</a></div><p class="ds-related-work--abstract ds2-5-body-sm">k-Means clustering algorithm is a heuristic algorithm that partitions the dataset into k clusters by minimizing the sum of squared distance in each cluster. In contrast, there are number of weaknesses. First it requires a prior knowledge of cluster number 'k'. Second it is sensitive to initialization which leads to random solutions. This paper presents a new approach to k-Means clustering by providing a solution to initial selection of cluster centroids and a dynamic approach based on silhouette validity index. Instead of running the algorithm for different values of k, the user need to give only initial value of k as k o as input and algorithm itself determines the right number of clusters for a given dataset. The algorithm is implemented in the MATLAB R2009b and results are compared with the original k-Means algorithm and other modified k-Means clustering algorithms. The experimental results demonstrate that our proposed scheme improves the initial center selection and overall computation time.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Means CLUSTERING ALGORITHM","attachmentId":31463168,"attachmentType":"pdf","work_url":"https://www.academia.edu/3807384/Means_CLUSTERING_ALGORITHM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/3807384/Means_CLUSTERING_ALGORITHM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="30868625" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/30868625/Performance_Enhancement_of_K_Means_Clustering_Algorithms_for_High_Dimensional_Data_sets">Performance Enhancement of K-Means Clustering Algorithms for High Dimensional Data sets</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="58754163" href="https://independent.academia.edu/smunankar">sudhir munankar</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm more effective and efficient; so as to get better clustering with reduced complexity. In this paper, the most delegate algorithms K-Means and enhanced K-Means were examined and analyzed based on their basic approach. The best algorithm in each category was found out based on their performance using Distance measure.These proposed algorithm is implemented and analyzed using a clustering tool WEKA. I. INTRODUCTION Clustering is a division of data into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Cluster analysis is a very important technology in Data Mining. It divides the datasets into several meaningful clusters to reflect the data sets' natural structure. Cluster is aggregation of data objects with common characteristics based on the measurement of some kind of information. There are several commonly used clustering algorithms, such as K-means, Density based and Hierarchical and so on. [2] Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups.[3] Clustering is an unsupervised classification mechanism where a set of patterns (data), usually multidimensional is classified into groups (clusters) such that members of one group are similar according to a predefined criterion. Clustering of a set forms a partition of its elements chosen to minimize some measure of dissimilarity between members of the same cluster .Clustering algorithms are often useful in various fields like data mining, pattern recognition, learning theory etc[14]. Terms: Cluster: A cluster is an ordered list of objects, which have some common characteristics. The objects belong to an interval [a, b], in our case [0, 1] Distance between Two Clusters: The distance between two clusters involves some or all elements of the two clusters. The clustering method determines how the distance should be computed. Similarity: A similarity measure SIMILAR (Di, Dj) can be used to represent the similarity between the documents. Typical similarity generates values of 0 for documents exhibiting no agreement among the assigned indexed terms, and 1 when perfect agreement is detected. Intermediate values are obtained for cases of partial agreement. Average Similarity: If the similarity measure is computed for all pairs of documents (Di, Dj) except when i=j, an average value AVERAGE SIMILARITY is obtainable. Specifically, AVERAGE SIMILARITY = CONSTANT SIMILAR (Di, Dj), where i=1, 2….n and j=1, 2….n and i < > j Threshold: The lowest possible input value of similarity required to join two objects in one cluster. Similarity Matrix: Similarity between objects calculated by the function SIMILAR (Di, Dj), represented in the form of a matrix is called a similarity matrix. Dissimilarity Coefficient: The dissimilarity coefficient of two clusters is defined to be the distance between them. The smaller the value of dissimilarity coefficient, the more similar two clusters are. Cluster Seed: First document or object of a cluster is defined as the initiator of that cluster i.e. every incoming object's similarity is compared with the initiator. The initiator is called the cluster seed.[6]</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Performance Enhancement of K-Means Clustering Algorithms for High Dimensional Data sets","attachmentId":51294809,"attachmentType":"pdf","work_url":"https://www.academia.edu/30868625/Performance_Enhancement_of_K_Means_Clustering_Algorithms_for_High_Dimensional_Data_sets","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/30868625/Performance_Enhancement_of_K_Means_Clustering_Algorithms_for_High_Dimensional_Data_sets"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="74414316" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/74414316/Programming_Techniques_in_Clustering_Algorithm">Programming Techniques in Clustering Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="173543822" href="https://independent.academia.edu/ShenbagaEzhil">Shenbaga Ezhil</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2013</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper mainly deals with the analysis of IPP in clustering. Clustering is exemplified by the unsupervised learning of patterns and clusters that may exist in a given database and is a useful tool for Knowledge Discovery in Database (KDD). A mathematical programming formulation of this problem is proposed that is theoretically justifiable and computationally implementable in a finite number of steps. The clustering algorithm applies the hierarchical clustering methodology [1] where points or clusters with the shortest distance are merged into a cluster until the desired number of clusters is achieved. Numerical examples are given for the above algorithmic approach. The Mathematical programming model subject to some constraints is a broad discipline that has been applied for various theoretical and applied problems. In this paper the described various integer programming model for clustering is implemented. The fundamental Non Linear programming problem, consists of minimizing the...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Programming Techniques in Clustering Algorithm","attachmentId":82575979,"attachmentType":"pdf","work_url":"https://www.academia.edu/74414316/Programming_Techniques_in_Clustering_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/74414316/Programming_Techniques_in_Clustering_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="31830726" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31830726/Clustering_with_K_Means_Algorithm">Clustering with K-Means Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="49309068" href="https://iitd.academia.edu/NAgarwal">Nishant Agarwal</a></div><p class="ds-related-work--abstract ds2-5-body-sm">There are many situations where we need to separate data into clusters without any labels being provided. This is an example of Unsupervised learning. In this assignment we apply K-Means algorithm for unsupervised learning on the given dataset and analyse the effect of various parameters including number of clusters and initialization method on the accuracy of clustering.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Clustering with K-Means Algorithm","attachmentId":52124895,"attachmentType":"pdf","work_url":"https://www.academia.edu/31830726/Clustering_with_K_Means_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31830726/Clustering_with_K_Means_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="44775107" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44775107/Analysis_of_K_Mean_Algorithm">Analysis of K-Mean Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64525554" href="https://technoscienceacademy.academia.edu/IJSRCSEIT">International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Clustering is one among the foremost common preliminary knowledge associates to analysis technique to get an intuition regarding the structure of the info. It is often outlined because the task of characteristic subgroups within the knowledge such knowledge points within the same subgroup (cluster) area unit are similar whereas knowledge points totally different in numerous clusters area different. There are several algorithms which deals with unsupervised learning. K means algorithm is one of such algorithm. Kmeans algorithm is an iterative algorithm that tries to partition the dataset into K pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the inter-cluster data points as similar as possible while also keeping the clusters as far as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid , that is (x2-x1)2+ (y2-y1)2</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysis of K-Mean Algorithm","attachmentId":65266286,"attachmentType":"pdf","work_url":"https://www.academia.edu/44775107/Analysis_of_K_Mean_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44775107/Analysis_of_K_Mean_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="86250599" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/86250599/A_Distance_Metric_for_Uneven_Clusters_of_Unsupervised_K_Means_Clustering_Algorithm">A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="8813857" href="https://independent.academia.edu/SesayAbu">Abu Sesay</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Access</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm","attachmentId":90746416,"attachmentType":"pdf","work_url":"https://www.academia.edu/86250599/A_Distance_Metric_for_Uneven_Clusters_of_Unsupervised_K_Means_Clustering_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/86250599/A_Distance_Metric_for_Uneven_Clusters_of_Unsupervised_K_Means_Clustering_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="50861047" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/50861047/A_DISTANCE_BASED_CLUSTERING_ALGORITHM">A DISTANCE BASED CLUSTERING ALGORITHM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="39122404" href="https://iaeme.academia.edu/publication">IAEME Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IAEME PUBLICATION, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">Clustering is an unsupervised data mining technique used to determine the objects that are similar in characteristics and group them together. K-means is a widely used partitional clustering algorithm but the performance of K-means strongly depends on the initial guess of centers (centroid) and the final cluster centroids may not be the optimal ones. Therefore it is important for K-means to have good choice of initial centroids. We have developed a clustering algorithm based on distance criteria to select a good set of initial centroids. Once some point d is selected as initial centroid, the proposed algorithm computes average of data points to avoid the points near to d from being selected as next initial centroids. These initial centroids are given as input to the K-means technique leading to a clustering algorithm that result in better clustering as compared to the K-means partition clustering algorithm, agglomerative hierarchical clustering algorithm and Hierarchical partitioning clustering algorithm.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A DISTANCE BASED CLUSTERING ALGORITHM","attachmentId":68738258,"attachmentType":"pdf","work_url":"https://www.academia.edu/50861047/A_DISTANCE_BASED_CLUSTERING_ALGORITHM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/50861047/A_DISTANCE_BASED_CLUSTERING_ALGORITHM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="26533021" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/26533021/An_Improved_Measure_for_Data_Clustering_in_High_Dimensional_Space_Keywords_Clustering_k_means_Clustering_Improved_k_means">An Improved Measure for Data Clustering in High Dimensional Space Keywords— Clustering; k-means Clustering; Improved k- means</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="50500942" href="https://isical.academia.edu/SnehalikaLall">Snehalika Lall</a></div><p class="ds-related-work--abstract ds2-5-body-sm">— The k-means clustering fails to correctly cluster the data points in high dimensional space, primarily for employing Euclidean norm as the distance metric. The Euclidean metric increases with the increase in data dimension, thus posing difficulty to segregate intra-cluster and inter-cluster data points. Adoption of k-means clustering, realized with Euclidean distance norm, often misguides the selection of cluster centres in a given iteration. This paper proposes a novel approach to k-means clustering algorithm by replacing the Euclidean distance metric by a new one. The merit of the proposed metric lies in keeping the distance low, even for large dimensional data points. The new metric enables the algorithm to correctly select the cluster centres over the iterations. Experiments undertaken revealed that the said distance metric based k-means clustering outperforms the traditional one by a large margin.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Improved Measure for Data Clustering in High Dimensional Space Keywords— Clustering; k-means Clustering; Improved k- means","attachmentId":46827462,"attachmentType":"pdf","work_url":"https://www.academia.edu/26533021/An_Improved_Measure_for_Data_Clustering_in_High_Dimensional_Space_Keywords_Clustering_k_means_Clustering_Improved_k_means","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/26533021/An_Improved_Measure_for_Data_Clustering_in_High_Dimensional_Space_Keywords_Clustering_k_means_Clustering_Improved_k_means"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":65935055,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":65935055,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_65935055" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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