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The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better\/competing results than well-known standard semi-supervised clustering methods.<\/jats:p>","DOI":"10.3233\/jifs-189744","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T13:39:31Z","timestamp":1616765971000},"page":"727-739","source":"Crossref","is-referenced-by-count":4,"title":["A New semi-supervised clustering for incomplete data"],"prefix":"10.1177","volume":"42","author":[{"given":"Sonia","family":"Goel","sequence":"first","affiliation":[{"name":"Scholar, USICT, GGSIPU, Delhi, India"},{"name":"Department of Electrical and Electronics Engineering, MSIT, Delhi, India"}]},{"given":"Meena","family":"Tushir","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, MSIT, Delhi, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189744_ref1","unstructured":"Bache K. 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