{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T09:12:39Z","timestamp":1721034759739},"reference-count":0,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,4,1]]},"abstract":"<p>Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.<\/p>","DOI":"10.4018\/ijrsda.2016040101","type":"journal-article","created":{"date-parts":[[2016,4,21]],"date-time":"2016-04-21T15:26:45Z","timestamp":1461252405000},"page":"1-9","source":"Crossref","is-referenced-by-count":6,"title":["Improving Efficiency of K-Means Algorithm for Large Datasets"],"prefix":"10.4018","volume":"3","author":[{"given":"Ch. Swetha","family":"Swapna","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India"}]},{"given":"V. Vijaya","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India"}]},{"given":"J.V.R","family":"Murthy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India"}]}],"member":"2432","container-title":["International Journal of Rough Sets and Data Analysis"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=150461","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T15:02:27Z","timestamp":1654095747000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJRSDA.2016040101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2016,4,1]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016,4]]}},"URL":"https:\/\/doi.org\/10.4018\/ijrsda.2016040101","relation":{},"ISSN":["2334-4598","2334-4601"],"issn-type":[{"value":"2334-4598","type":"print"},{"value":"2334-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,1]]}}}