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We analyzed the corresponding positions distribution of similar fingerprints, and then found that the fuzzy similarity between fingerprints is the root cause of the larger errors existing. According to clusters distribution feature of corresponding positions of the similar fingerprints, we proposed a K-Means+ clustering algorithm to achieve fine-grained fingerprint positioning. Due to the K-Means+ algorithm failing to locate the positions of outliers, we also designed a linear sequence matching algorithm to improve the outliers positioning, and reduce the impact of fuzzy similarity. Experimental results illustrate that our algorithm can get a maximum positioning error less than 5\u2009m, which outperforms other algorithms. Meanwhile, all the positioning errors over 4\u2009m in our algorithm are less than 2%. The positioning accuracy has been improved significantly. <\/jats:p>","DOI":"10.1155\/2015\/753191","type":"journal-article","created":{"date-parts":[[2015,8,13]],"date-time":"2015-08-13T21:03:33Z","timestamp":1439499813000},"page":"753191","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Fuzzy Similarity Elimination Algorithm for Indoor Fingerprint Positioning"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-1000-1109","authenticated-orcid":false,"given":"Yongle","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing 100876, 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