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However, it still has problems when processing a complex data set with irregular shapes and varying densities to get a good clustering result with anomaly detection. A density fragment clustering (DFC) algorithm without peaks algorithm is proposed with inspiration from DPC, DBSCAN and SCAN to cope with a larger number of data sets. Experimental results show that our algorithm is more feasible and effective when compared to DPC, AP and DBSCAN algorithms.<\/jats:p>","DOI":"10.3233\/jifs-17678","type":"journal-article","created":{"date-parts":[[2018,1,19]],"date-time":"2018-01-19T11:00:07Z","timestamp":1516359607000},"page":"525-536","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["DFC: Density Fragment Clustering without Peaks"],"prefix":"10.1177","volume":"34","author":[{"given":"Jianhua","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Management, Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York, New Paltz, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,1,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.845141"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1136800"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TSMCC.2008.2007252","article-title":"A survey of evolutionary algorithms for clustering","volume":"39","author":"Hruschka E.R.","year":"2009","unstructured":"HruschkaE.R., CampelloR.J., FreitasA.A. and De CarvalhoA., A survey of evolutionary algorithms for clustering, IEEE Transactions on Systems, Man, and Cybernetics 39 (2009), 133\u2013155.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.95.25.14863"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"e_1_3_2_7_2","first-page":"268","article-title":"A survey of clustering algorithms","volume":"16","author":"Rokach L.","year":"2009","unstructured":"RokachL., A survey of clustering algorithms, Data Mining and Knowledge Discovery Handbook 16 (2009), 268\u2013298.","journal-title":"Data Mining and Knowledge Discovery Handbook"},{"key":"e_1_3_2_8_2","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"Ester M.","year":"1996","unstructured":"EsterM., KriegelH.P. and XuX., A density-based algorithm for discovering clusters in large spatial databases with noise, Knowledge Discovery and Data Mining (1996), 226\u2013231.","journal-title":"Knowledge Discovery and Data Mining"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009745219419"},{"key":"e_1_3_2_10_2","first-page":"47","volume-title":"Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data","author":"Ertoz L.","year":"2005","unstructured":"ErtozL., SteinbachM. and KumarV., Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data, SIAM International Conference on Data Mining (2005), 47\u201358."},{"key":"e_1_3_2_11_2","first-page":"471","article-title":"Data mining","author":"Han J.","year":"2011","unstructured":"HanJ., KamberM. and PeiJ., Data mining, Concepts and Techniques (2011), 471\u2013473.","journal-title":"Concepts and Techniques"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242072"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.400568"},{"key":"e_1_3_2_14_2","first-page":"1","article-title":"Finding groups in data: An introduction to cluster analysis","author":"Kaufman L.","year":"2005","unstructured":"KaufmanL. and RousseeuwP.J., Finding groups in data: An introduction to cluster analysis, Journal of the American Statistical Association (2005), 1\u201315.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.knosys.2016.02.001","article-title":"Study on density peaks clustering based on k-nearest neighbors and principal component analysis","volume":"99","author":"Du M.","year":"2016","unstructured":"DuM., DingS. and JiaH., Study on density peaks clustering based on k-nearest neighbors and principal component analysis, Knowledge Based Systems 99 (2016), 135\u2013145.","journal-title":"Knowledge Based Systems"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.11.091"},{"key":"e_1_3_2_17_2","unstructured":"ZhangW. and LiJ. 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