{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:51:03Z","timestamp":1781110263452,"version":"3.54.1"},"reference-count":29,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.<\/jats:p>","DOI":"10.4018\/ijkdb.2018010104","type":"journal-article","created":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T09:42:39Z","timestamp":1521020559000},"page":"42-59","source":"Crossref","is-referenced-by-count":1,"title":["Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology"],"prefix":"10.4018","volume":"8","author":[{"given":"Deepali","family":"Virmani","sequence":"first","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Jain","sequence":"additional","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketan","family":"Parikh","sequence":"additional","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shefali","family":"Upadhyaya","sequence":"additional","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Srivastav","sequence":"additional","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJKDB.2018010104-0","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6377(02)00162-1"},{"key":"IJKDB.2018010104-1","article-title":"Analysis and Approach: K-Means and K-Medoids Data Mining Algorithms.","author":"A.Batra","year":"2011","journal-title":"5th IEEE International Conference on Advanced Computing & Communication Technologies [ICACCT\u20102011]"},{"key":"IJKDB.2018010104-2","unstructured":"Bl\u00f6mer, J., Brauer, S., & Bujna, K. 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CS5350\/6350: Machine Learning Oct, 4, 24."},{"issue":"6","key":"IJKDB.2018010104-22","article-title":"New Method for Finding Initial Cluster Centroids in K-means Algorithm.","volume":"74","author":"H.Singh","year":"2013","journal-title":"International Journal of Computers and Applications"},{"key":"IJKDB.2018010104-23","unstructured":"ttnphns. (2012, January 17). ttnphns\u2019s Answer to Are mean normalization and feature scaling needed for k-means clustering? Stackexchange. Retrieved from https:\/\/stats.stackexchange.com\/questions\/21222\/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering"},{"key":"IJKDB.2018010104-24","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2010.363.368"},{"key":"IJKDB.2018010104-25","unstructured":"Virmani, D., Taneja, S., & Malhotra, G. (2015). Normalization based K means Clustering Algorithm. arXiv:1503.00900"},{"key":"IJKDB.2018010104-26","unstructured":"Wikipedia. (n.d.). k-means Clustering. 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