Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Data Classification and Incremental Clustering Using Unsupervised Learning Description An account of the resource Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Chakraborty S.; Islam S.H.; Samanta D. Source A related resource from which the described resource is derived EAI/Springer Innovations in Communication and Computing, pp. 73-99. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-030-93088-2_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-93088-2_4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130556713&amp;doi=10.1007%2F978-3-030-93088-2_4&amp;partnerID=40&amp;md5=6a07db7dff2e32b398e48cf9db3863c8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130556713&amp;doi=10.1007%2f978-3-030-93088-2_4&amp;partnerID=40&amp;md5=6a07db7dff2e32b398e48cf9db3863c8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25228595 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India