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The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pattern extraction. The proposed approach mainly focuses on anomaly detection in qualitative data with multiple granules. The approximations of the dataset will be derived through multiequivalence relation, and then, the rough set-based entropy measure with weighted density method is applied on every object and attribute. For detecting outliers, threshold value fixation is performed based on the estimated weight. The performance of the algorithm is evaluated and compared with existing outlier detection algorithms. Datasets such as breast cancer, chess, and car evaluation have been taken from the UCI repository to prove its efficiency and performance.<\/jats:p>","DOI":"10.1515\/comp-2020-0228","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T12:10:10Z","timestamp":1647864610000},"page":"123-133","source":"Crossref","is-referenced-by-count":3,"title":["Rough set-based entropy measure with weighted density outlier detection method"],"prefix":"10.1515","volume":"12","author":[{"given":"Tamilarasu","family":"Sangeetha","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India"}]},{"given":"Amalanathan","family":"Geetha Mary","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India"}]}],"member":"374","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"2022081707553225280_j_comp-2020-0228_ref_001","doi-asserted-by":"crossref","unstructured":"R. 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