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Hard clustering (KMeans Clustering) or Soft clustering (Fuzzy C-Means) to generate samples called clusters, which in turn is used to generate the Local Regression Models (LRM) for the given dataset. These LRMs are used to create a Global Regression Model. This methodology is known as Enhanced Regression Model (ERM). The performance of the proposed approach is tested with 5 different datasets. The experimental results revealed that the proposed methodology yielded better predictive accuracy than the non-hybrid MLR model; also, fuzzy C-Means performs better than the KMeans clustering algorithm for sample selection. Thus, ERM has potential to handle data with uncertainty and complex pattern and produced a high prediction accuracy rate.<\/jats:p>","DOI":"10.3233\/jifs-211736","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T11:23:16Z","timestamp":1662117796000},"page":"3059-3069","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced regression model using cluster based sampling techniques"],"prefix":"10.1177","volume":"44","author":[{"given":"S.","family":"Dhamodharavadhani","sequence":"first","affiliation":[{"name":"Department of Computer Science, Periyar University, Salem, Tamil Nadu, India"}]},{"given":"R.","family":"Rathipriya","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Periyar University, Salem, Tamil Nadu, India"}]}],"member":"179","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"CohenJ. 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