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In this study, a deterministic approach which do not requires user defined parameters during clustering; can generate overlapped and non-overlapped clusters and detect outliers has been proposed. Here, a minimum support value has been adopted from association rule mining to improve the clustering results. Further, the improved approach has been analysed on artificial and real datasets. The results demonstrated that datasets are well clustered with this approach too and it achieved success to generate almost same number of clusters as present in real datasets.<\/jats:p>","DOI":"10.4018\/ijismd.2019010103","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T14:25:36Z","timestamp":1553696736000},"page":"42-59","source":"Crossref","is-referenced-by-count":12,"title":["Adaptive Threshold Based Clustering"],"prefix":"10.4018","volume":"10","author":[{"given":"Mamta","family":"Mittal","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, G. B. Pant Govt. 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