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Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains.<\/jats:p>","DOI":"10.3233\/jifs-169701","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T13:25:39Z","timestamp":1529069139000},"page":"1633-1644","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["ARSkNN: An efficient k-nearest neighbor classification technique using mass based similarity measure"],"prefix":"10.1177","volume":"35","author":[{"given":"Ashish","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India"}]},{"given":"Roheet","family":"Bhatnagar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India"}]},{"given":"Sumit","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Manipal University Jaipur, Jaipur, India"}]}],"member":"179","published-online":{"date-parts":[[2018,6,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-017-2053-3_1"},{"key":"e_1_3_1_3_2","unstructured":"AlimogluF. and AlpaydinE. 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