{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:40:18Z","timestamp":1781102418880,"version":"3.54.1"},"reference-count":27,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10]]},"abstract":"<jats:p>Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.<\/jats:p>","DOI":"10.4018\/ijitwe.2019100104","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T08:38:04Z","timestamp":1565167084000},"page":"64-78","source":"Crossref","is-referenced-by-count":0,"title":["An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model"],"prefix":"10.4018","volume":"14","author":[{"family":"Ravichandran M","sequence":"first","affiliation":[{"name":"Shadan College of Engineering and Technolgoy, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Subramanian K M","sequence":"additional","affiliation":[{"name":"Shadan College of Engineering and Technolgoy, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Jothikumar R","sequence":"additional","affiliation":[{"name":"Shadan College of Engineering and Technology, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJITWE.2019100104-0","doi-asserted-by":"publisher","DOI":"10.4018\/jitwe.2006010102"},{"key":"IJITWE.2019100104-1","doi-asserted-by":"publisher","DOI":"10.4018\/jitwe.2011070103"},{"key":"IJITWE.2019100104-2","doi-asserted-by":"publisher","DOI":"10.4018\/jitwe.2012100102"},{"key":"IJITWE.2019100104-3","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/978-81-322-2135-7_67","article-title":"Multiview Clustering in Heterogeneous Environment","volume":"325","author":"A.Bharathi","year":"2014","journal-title":"Artificial Intelligence and Evolutionary Algorithms in Engineering Systems"},{"key":"IJITWE.2019100104-4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.94"},{"key":"IJITWE.2019100104-5","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623734"},{"key":"IJITWE.2019100104-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.10.016"},{"key":"IJITWE.2019100104-7","doi-asserted-by":"publisher","DOI":"10.1023\/B:APIN.0000021416.41043.0f"},{"key":"IJITWE.2019100104-8","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-014-0307-6"},{"key":"IJITWE.2019100104-9","first-page":"1","article-title":"Constrained Inference for Multi-View Clustering","author":"S.Joshi","year":"2014","journal-title":"International Conference on Machine Learning"},{"key":"IJITWE.2019100104-10","doi-asserted-by":"publisher","DOI":"10.1145\/2501006.2501010"},{"key":"IJITWE.2019100104-11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.95"},{"key":"IJITWE.2019100104-12","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-015-1682-7"},{"key":"IJITWE.2019100104-13","doi-asserted-by":"publisher","DOI":"10.4018\/jitwe.2013040102"},{"key":"IJITWE.2019100104-14","doi-asserted-by":"publisher","DOI":"10.4018\/IJITWE.2018010101"},{"key":"IJITWE.2019100104-15","doi-asserted-by":"publisher","DOI":"10.1007\/s12038-015-9559-8"},{"key":"IJITWE.2019100104-16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2503743"},{"key":"IJITWE.2019100104-17","first-page":"352","article-title":"Multi-view clustering and feature learning via structured sparsity.","author":"H.Wang","year":"2013","journal-title":"International conference on machine learning"},{"key":"IJITWE.2019100104-18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46672-9_49"},{"key":"IJITWE.2019100104-19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22053-6_13"},{"key":"IJITWE.2019100104-20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.006"},{"key":"IJITWE.2019100104-21","unstructured":"Wang, Y., Zhang, W., Wu, L., Lin, X., Fang, M., & Pan, S. 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