{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:28:17Z","timestamp":1740148097777,"version":"3.37.3"},"reference-count":18,"publisher":"Wiley","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\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61272029","KT13013"],"award-info":[{"award-number":["61272029","KT13013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bureau of Statistics of Shandong Province","award":["61272029","KT13013"],"award-info":[{"award-number":["61272029","KT13013"]}]},{"DOI":"10.13039\/501100005022","name":"Beijing Jiaotong University","doi-asserted-by":"publisher","award":["61272029","KT13013"],"award-info":[{"award-number":["61272029","KT13013"]}],"id":[{"id":"10.13039\/501100005022","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.<\/jats:p>","DOI":"10.1155\/2015\/828057","type":"journal-article","created":{"date-parts":[[2015,1,21]],"date-time":"2015-01-21T16:02:19Z","timestamp":1421856139000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["An Extended Affinity Propagation Clustering Method Based on Different Data Density Types"],"prefix":"10.1155","volume":"2015","author":[{"given":"XiuLi","family":"Zhao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing 100044, China"},{"name":"Business School, Qilu University of Technology, Jinan 250353, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"WeiXiang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1136800"},{"first-page":"188","volume-title":"Spatial clustering methods in data mining","year":"2001","key":"2"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/11604655_59"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-006-9953-7"},{"issue":"4","key":"5","first-page":"20","volume":"41","year":"2011","journal-title":"Journal of Shandong University (Engineering Science)"},{"issue":"23","key":"6","first-page":"22","volume":"36","year":"2010","journal-title":"Computer Engineering"},{"issue":"23","key":"7","first-page":"197","volume":"33","year":"2007","journal-title":"Computer Engineering"},{"issue":"4","key":"8","first-page":"624","volume":"36","year":"2010","journal-title":"Journal of Southwest University for Nationalities: Natural Science"},{"issue":"10","key":"10","first-page":"1733","volume":"47","year":"2010","journal-title":"Journal of Computer Research and Development"},{"issue":"3","key":"12","first-page":"841","volume":"27","year":"2010","journal-title":"Application Research of Computers"},{"issue":"3","key":"13","first-page":"33","volume":"21","year":"2009","journal-title":"Journal of Naval University of Engineering"},{"issue":"1","key":"14","first-page":"268","year":"2010","journal-title":"Computer Science"},{"year":"1980","key":"15"},{"year":"1989","key":"16"},{"issue":"3","key":"17","first-page":"292","volume":"25","year":"2003","journal-title":"Acta Seismologica Sinica"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1080\/13658810500399654"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2011.9727862"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1108\/k-03-2013-0048"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/828057.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/828057.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2015\/828057.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,7,27]],"date-time":"2016-07-27T16:38:22Z","timestamp":1469637502000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/cin\/2015\/828057\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":18,"alternative-id":["828057","828057"],"URL":"https:\/\/doi.org\/10.1155\/2015\/828057","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2015]]}}}