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The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding \u201cthreshold setting\u201d and \u201ccutoff\u201d are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.<\/jats:p>","DOI":"10.1515\/jisys-2015-0004","type":"journal-article","created":{"date-parts":[[2015,10,13]],"date-time":"2015-10-13T20:04:54Z","timestamp":1444766694000},"page":"595-610","source":"Crossref","is-referenced-by-count":16,"title":["Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation"],"prefix":"10.1515","volume":"25","author":[{"given":"Vijay","family":"Kumar","sequence":"first","affiliation":[{"name":"Thapar University, Patiala, Punjab, India"}]},{"given":"Jitender Kumar","family":"Chhabra","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Kurukshetra, Haryana, India"}]},{"given":"Dinesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Guru Jambheshwer University of Science and Technology, Hisar, Haryana, India"}]}],"member":"374","published-online":{"date-parts":[[2015,10,13]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"R. 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