{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T14:52:17Z","timestamp":1770475937245,"version":"3.49.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T00:00:00Z","timestamp":1537747200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,11,20]]},"abstract":"<jats:p>In view of intelligent Minkowski metric Weighted K-means (iMWK) sensitive to feature weighting, a novel clustering technique called intelligent Minkowski metric feature weights subspace clustering algorithms through hybrid dissimilarity measure (iMWK-HD) is presented. First, a new optimization objective function is constructed by incorporating the Minkowski distance and Cosine dissimilarity in the subspace. Based on this objective function, the corresponding update rules for clustering are then derived, followed by the development of the novel iMWK-HD algorithm. The properties of this algorithm are investigated and the performance is evaluated experimentally using synthetic and UCI datasets. The experimental studies demonstrate that the accuracy of the proposed iMWK-HD algorithm outperforms three existing clustering algorithms, i.e., iK-means, iWK-means and iMWK-means. In addition, the proposed algorithms are immune to irrelevant features in cluster subspace.<\/jats:p>","DOI":"10.3233\/jifs-18563","type":"journal-article","created":{"date-parts":[[2018,9,29]],"date-time":"2018-09-29T07:22:38Z","timestamp":1538205758000},"page":"5541-5556","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhanced subspace clustering through combining Minkowski distance and Cosine dissimilarity"],"prefix":"10.1177","volume":"35","author":[{"given":"Liying","family":"Jin","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an Jiaotong University, Shannxi Xi\u2019an, China"}]},{"given":"Xiaobin","family":"Zhi","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2019an University of Post and Telecommunications, Shannxi Xi\u2019an, China"}]},{"given":"Shengdun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an Jiaotong University, Shannxi Xi\u2019an, China"}]}],"member":"179","published-online":{"date-parts":[[2018,9,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.06.039"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29807-3"},{"key":"e_1_3_2_5_2","first-page":"109","volume":"72","author":"Mirkin B.","year":"2012","unstructured":"B.Mirkin, Clustering: A data recovery approach, Chapman & Hall Crc Computer Science & Data Analysis 72 (2012), 109\u2013110.","journal-title":"Clustering: A data recovery approach"},{"key":"e_1_3_2_6_2","first-page":"1","article-title":"A robust fuzzy clustering algorithm using mean-field-approximation based hidden Markov random field model for image segmentation","volume":"32","author":"Chen A.","year":"2016","unstructured":"A.Chen and S.Wang, A robust fuzzy clustering algorithm using mean-field-approximation based hidden Markov random field model for image segmentation, Journal of Intelligent & Fuzzy Systems 32 (2016), 1\u201312.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"e_1_3_2_7_2","first-page":"281","article-title":"Some Methods for Classification and Analysis of Multivariate Observations","author":"Macqueen J.","year":"1967","unstructured":"J.Macqueen, Some Methods for Classification and Analysis of Multivariate Observations, Proc of Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281\u2013297.","journal-title":"Proc of Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"e_1_3_2_8_2","unstructured":"MATLAB Version 7.10.0 (R2010a) The MathWorks Inc. Natick Massachusetts 2010."},{"key":"e_1_3_2_9_2","author":"Field A.","year":"2005","unstructured":"A.Field, Discovering statistics using SPSS, SAGE Publications, 2005.","journal-title":"Discovering statistics using SPSS"},{"key":"e_1_3_2_10_2","first-page":"12","article-title":"R: A language and environment for statistical computing","volume":"1","author":"Null R.C.T.R.","year":"2013","unstructured":"R.C.T.R.Null, R.Team, R.C.T.Null, T.Core Writing, R.Null, R.Team, R.D.C.T.Null, R.Core, R.Team and R.D.C.Team, R: A language and environment for statistical computing, Computing 1 (2013), 12\u201321.","journal-title":"Computing"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.02.001"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00357-001-0018-x"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.95"},{"key":"e_1_3_2_14_2","first-page":"943","volume":"37","author":"Chan E.","year":"2003","unstructured":"E.Chan, W.Ching, M.Ng and J.Huang, An optimization algorithm for clustering using weighted dissimilarity measures \u22c6 Pattern Recognition 37 (2003), 943\u2013952.","journal-title":"An optimization algorithm for clustering using weighted dissimilarity measures \u22c6 Pattern Recognition"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.01.034"},{"key":"e_1_3_2_16_2","first-page":"700","volume":"700","author":"Amorim R.C.D.","year":"2016","unstructured":"R.C.D.Amorim, Applying subclustering and Lp distance in Weighted K-Means with distributed centroids, 700 (2016), 700\u2013707.","journal-title":"Applying subclustering and Lp distance in Weighted K-Means with distributed centroids"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.10.018"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-152647"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.12.074"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.08.012"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.05.029"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.03.002"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207160.2014.958079"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-141517"},{"key":"e_1_3_2_25_2","first-page":"181","article-title":"Non-Euclidean norms and data normalization, Esann 2004","volume":"2004","author":"Doherty K.","year":"2004","unstructured":"K.Doherty, R.Adams and N.Davey, Non-Euclidean norms and data normalization, Esann 2004, European Symposium on Artificial Neural Networks, Bruges, Belgium, Proceedings 2004, 2004, pp. 181\u2013186.","journal-title":"European Symposium on Artificial Neural Networks, Bruges, Belgium, Proceedings"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.872551"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.1037"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00357-010-9049-5"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781584888796.ch10"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1971.10482356"},{"key":"e_1_3_2_31_2","author":"Murphy P.","year":"1998","unstructured":"P.Murphy and D.W.Aha, UCI repository of machine learning databases-a machine-readable repository, 1998.","journal-title":"UCI repository of machine learning databases-a machine-readable repository"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1977.4309789"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18563","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-18563","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18563","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T19:24:42Z","timestamp":1770405882000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-18563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,24]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,11,20]]}},"alternative-id":["10.3233\/JIFS-18563"],"URL":"https:\/\/doi.org\/10.3233\/jifs-18563","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,24]]}}}