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Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain\u2019s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.<\/jats:p>","DOI":"10.1515\/jisys-2014-0172","type":"journal-article","created":{"date-parts":[[2015,7,13]],"date-time":"2015-07-13T04:57:51Z","timestamp":1436763471000},"page":"431-440","source":"Crossref","is-referenced-by-count":6,"title":["DBSCANI: Noise-Resistant Method for Missing Value Imputation"],"prefix":"10.1515","volume":"25","author":[{"given":"Archana","family":"Purwar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, JIIT Noida, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandeep Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, JIIT Noida, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2015,7,10]]},"reference":[{"key":"2025120523262708552_j_jisys-2014-0172_ref_001_w2aab3b7d242b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"R. 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