{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T20:02:01Z","timestamp":1649102521692},"reference-count":24,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it.<\/jats:p>","DOI":"10.1155\/2015\/829201","type":"journal-article","created":{"date-parts":[[2015,6,28]],"date-time":"2015-06-28T21:02:52Z","timestamp":1435525372000},"page":"1-16","source":"Crossref","is-referenced-by-count":0,"title":["Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation"],"prefix":"10.1155","volume":"2015","author":[{"given":"Xiao","family":"Sun","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for E-Commerce Technology, Tsinghua University, Beijing 100084, China"},{"name":"DNSLAB, China Internet Network Information Center, Beijing 100190, China"}]},{"given":"Tongda","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Yueting","family":"Chai","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for E-Commerce Technology, Tsinghua University, Beijing 100084, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for E-Commerce Technology, Tsinghua University, Beijing 100084, China"}]}],"member":"98","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1038\/455001a"},{"key":"2","year":"2005"},{"key":"3","year":"2006"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3414\/ME9103"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1111\/j.1744-7917.2012.01519.x"},{"key":"7","first-page":"643","volume-title":"Clinical decision-support systems","year":"2014"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1974.10480191"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-67740-3_3"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.02.012"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(93)90135-J"},{"key":"15","year":"2000"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2010.11.002"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodqual.2011.11.002"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodqual.2004.04.004"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1108\/bfj-09-2011-0215"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22729-5_17"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2010.01.003"},{"issue":"3","key":"23","first-page":"78","volume":"19","year":"2001","journal-title":"Journal of Park and Recreation Administration"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/s0148-2963(02)00357-0"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.2224\/sbp.2012.40.3.401"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1217600"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/829201.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/829201.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/829201.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,7,27]],"date-time":"2016-07-27T22:02:45Z","timestamp":1469656965000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2015\/829201\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":24,"alternative-id":["829201","829201"],"URL":"https:\/\/doi.org\/10.1155\/2015\/829201","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]}}}