{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:28:50Z","timestamp":1772166530347,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Xi\u2019an Research Institute of High-Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.<\/jats:p>","DOI":"10.1186\/s13638-020-01779-1","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T03:03:04Z","timestamp":1599102184000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial immune network clustering based on a cultural algorithm"],"prefix":"10.1186","volume":"2020","author":[{"given":"Liyuan","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"issue":"2","key":"1779_CR1","doi-asserted-by":"crossref","first-page":"128","DOI":"10.26599\/BDMA.2018.9020012","volume":"1","author":"C Zhang","year":"2018","unstructured":"C. Zhang, M. Yang, J. Lv, W. Yang, An improved hybrid collaborative filtering algorithm based on tags and time factor. Big Data Mining Analytics 1(2), 128\u2013136 (2018)","journal-title":"Big Data Mining Analytics"},{"key":"1779_CR2","doi-asserted-by":"crossref","unstructured":"H. Liu, H. Kou, C. Yan, L. Qi, Link prediction in paper citation network to construct paper correlated graph. EURASIP J Wireless Commun Network Article number 233 (2019)","DOI":"10.1186\/s13638-019-1561-7"},{"key":"1779_CR3","doi-asserted-by":"crossref","unstructured":"F. Marcantoni, M. Diamantaris, S. Ioannidis, J. Polakis. A Large-scale study on the risks of the Html5 WebAPI for mobile sensor-based attacks. In Proc. of World Wide Web Conference (WWW\u201919), ACM Press, New York, pp. 3063\u20133071 (2019)","DOI":"10.1145\/3308558.3313539"},{"key":"1779_CR4","doi-asserted-by":"publisher","unstructured":"X. Chi, C. Yan, H. Wang, W. Rafique, L. Qi, Amplified LSH-based recommender systems with privacy protection. concurrency and computation: practice and experience (2020). https:\/\/doi.org\/10.1002\/CPE.5681","DOI":"10.1002\/CPE.5681"},{"key":"1779_CR5","doi-asserted-by":"crossref","first-page":"105830","DOI":"10.1016\/j.asoc.2019.105830","volume":"85","author":"N Almarimi","year":"2019","unstructured":"N. Almarimi, A. Ouni, S. Bouktif, M.W. Mkaouer, R.G. Kula, M.A. Saied, Web service API recommendation for automated mashup creation using multi-objective evolutionary search. Appl. Soft Comput. 85, 105830 (2019)","journal-title":"Appl. Soft Comput."},{"key":"1779_CR6","doi-asserted-by":"publisher","unstructured":"W. Zhong, X. Yin, X. Zhang, S. Li, W. Dou, R. Wang, L. Qi, Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment. Comput. Commun. (2020). https:\/\/doi.org\/10.1016\/j.comcom.2020.04.018","DOI":"10.1016\/j.comcom.2020.04.018"},{"key":"1779_CR7","doi-asserted-by":"publisher","unstructured":"X. Xu, R. Mo, F. Dai, W. Lin, S. Wan, W. Dou, Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud. IEEE Transactions on Industrial Informatics (2019). https:\/\/doi.org\/10.1109\/TII.2019.2959258","DOI":"10.1109\/TII.2019.2959258"},{"issue":"3","key":"1779_CR8","doi-asserted-by":"crossref","first-page":"159","DOI":"10.26599\/BDMA.2019.9020006","volume":"2","author":"BS Jena","year":"2019","unstructured":"B.S. Jena, C. Khan, R. Sunderraman, High performance frequent subgraph mining on transaction datasets: A survey and performance comparison. Big Data Mining Analytics 2(3), 159\u2013180 (2019)","journal-title":"Big Data Mining Analytics"},{"key":"1779_CR9","doi-asserted-by":"publisher","unstructured":"Y. Zhang, K. Wang, Q. He, Covering-based web service quality prediction via neighborhood-aware matrix factorization, IEEE transactions on services computing. (2019). https:\/\/doi.org\/10.1109\/TSC.2019.2891517","DOI":"10.1109\/TSC.2019.2891517"},{"key":"1779_CR10","doi-asserted-by":"publisher","unstructured":"Y. Zhang, G. Cui, S. Deng, Efficient query of quality correlation for service composition. IEEE Trans. Serv. Comput.. https:\/\/doi.org\/10.1109\/TSC.2018.2830773,2018","DOI":"10.1109\/TSC.2018.2830773,2018"},{"key":"1779_CR11","doi-asserted-by":"publisher","unstructured":"Y. Zhang, C. Yin, Q. Wu, et al., Location-Aware Deep Collaborative Filtering for Service Recommendation, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019). https:\/\/doi.org\/10.1109\/TSMC.2019.2931723","DOI":"10.1109\/TSMC.2019.2931723"},{"key":"1779_CR12","doi-asserted-by":"publisher","unstructured":"Y. Chen, N. Zhang, Y. Zhang, X. Chen, W. Wu, X.S. Shen, Energy efficient dynamic offloading in mobile edge computing for internet of things. IEEE Transact Cloud Comput (2019). https:\/\/doi.org\/10.1109\/TCC.2019.2898657","DOI":"10.1109\/TCC.2019.2898657"},{"key":"1779_CR13","first-page":"1","volume":"92","author":"J Li","year":"2020","unstructured":"J. Li, T. Cai, K. Deng, X. Wang, T. Sellis, F. Xia, Community-diversified influence maximization in social networks. Inf. Syst. 92, 1\u201312 (2020)","journal-title":"Inf. Syst."},{"key":"1779_CR14","doi-asserted-by":"publisher","unstructured":"T. Cai, J. Li, A.S. Mian, R. Li, T. Sellis, J.X. Yu, Target-aware holistic influence maximization in spatial social networks. IEEE Trans. Knowl. Data Eng. (2020). https:\/\/doi.org\/10.1109\/TKDE.2020.3003047","DOI":"10.1109\/TKDE.2020.3003047"},{"issue":"2","key":"1779_CR15","doi-asserted-by":"crossref","first-page":"100","DOI":"10.26599\/BDMA.2018.9020034","volume":"2","author":"J He","year":"2019","unstructured":"J. He, M. Han, S. Ji, T. Du, Z. Li, Spreading social influence with both positive and negative opinions in online networks. Big Data Mining Analytics 2(2), 100\u2013117 (2019)","journal-title":"Big Data Mining Analytics"},{"issue":"1","key":"1779_CR16","doi-asserted-by":"crossref","first-page":"86","DOI":"10.26599\/TST.2018.9010002","volume":"24","author":"G Li","year":"2019","unstructured":"G. Li, S. Peng, C. Wang, J. Niu, Y. Yuan, An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks. Tsinghua Sci. Technol. 24(1), 86\u201396 (2019)","journal-title":"Tsinghua Sci. Technol."},{"issue":"3","key":"1779_CR17","doi-asserted-by":"crossref","first-page":"271","DOI":"10.26599\/TST.2018.9010124","volume":"24","author":"L Liu","year":"2019","unstructured":"L. Liu, X. Chen, Z. Lu, L. Wang, X. Wen, Mobile-edge computing framework with data compression for wireless network in energy internet. Tsinghua Sci. Technol. 24(3), 271\u2013280 (2019)","journal-title":"Tsinghua Sci. Technol."},{"key":"1779_CR18","doi-asserted-by":"publisher","unstructured":"X. Xu, Y. Chen, X. Zhang, Q. Liu, X. Liu, L. Qi, A Blockchain-Based Computation Offloading Method for Edge Computing in 5G Networks. Software: Practice and Experience (2019). https:\/\/doi.org\/10.1002\/spe.2749","DOI":"10.1002\/spe.2749"},{"issue":"1","key":"1779_CR19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.26599\/TST.2018.9010067","volume":"24","author":"Y Huang","year":"2019","unstructured":"Y. Huang, Y. Chai, Y. Liu, J. Shen, Architecture of next-generation e-commerce platform. Tsinghua Sci. Technol. 24(1), 18\u201329 (2019)","journal-title":"Tsinghua Sci. Technol."},{"issue":"10","key":"1779_CR20","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1080\/00207160802676588","volume":"87","author":"K Yang","year":"2010","unstructured":"K. Yang, K. Maginu, H. Nomura, Cultural algorithm and their application. Int. J. Comput. Math. 87(10), 2143\u20132157 (2010)","journal-title":"Int. J. Comput. Math."},{"key":"1779_CR21","doi-asserted-by":"crossref","unstructured":"H. Liu, H. Kou, C. Yan and L. Qi. Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph. Complexity, Volume 2020, Article ID 2085638, 15 pages, 2020.","DOI":"10.1155\/2020\/2085638"},{"issue":"1","key":"1779_CR22","first-page":"228","volume":"35","author":"J Qian","year":"2015","unstructured":"J. Qian, M. Ji, A quantum-inspired evolutionary algorithm based on culture and knowledge. System Eng Theory Practice 35(1), 228\u2013238 (2015)","journal-title":"System Eng Theory Practice"},{"key":"1779_CR23","doi-asserted-by":"publisher","unstructured":"L. Qi, Q. He, F. Chen, X. Zhang, W. Dou, Q. Ni, Data-driven web APIS recommendation for building web applications. IEEE Transact Big Data (2020). https:\/\/doi.org\/10.1109\/TBDATA.2020.2975587","DOI":"10.1109\/TBDATA.2020.2975587"},{"issue":"2","key":"1779_CR24","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TSMCB.2010.2068046","volume":"41","author":"M Daneshyari","year":"2011","unstructured":"M. Daneshyari, G.G. Yen, Culture-based multiobjective particle swarm optimization. IEEE Transact Syst Man Cybernet B Cybern 41(2), 553\u2013567 (2011)","journal-title":"IEEE Transact Syst Man Cybernet B Cybern"},{"issue":"3","key":"1779_CR25","first-page":"117","volume":"33","author":"BZ Qiu","year":"2012","unstructured":"B.Z. Qiu, Y. Yang, X.W. Du, BRINK: An algorithm of boundary points of clusters detection based on local qualitative factor. J Zhengzhou Univ Eng Sci 33(3), 117\u2013121 (2012)","journal-title":"J Zhengzhou Univ Eng Sci"},{"issue":"1","key":"1779_CR26","first-page":"171","volume":"30","author":"X Li","year":"2015","unstructured":"X. Li, P. Geng, B. Qiu, Clustering boundary points detection technology for attribute data set. Control and Decision 30(1), 171\u2013175 (2015)","journal-title":"Control and Decision"},{"key":"1779_CR27","doi-asserted-by":"publisher","unstructured":"X. Xu, X. Zhang, X. Liu, J. Jiang, L. Qi, M.Z.A. Bhuiyan, Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles. IEEE Trans. Intell. Transp. Syst. (2020). https:\/\/doi.org\/10.1109\/TITS.2020.2982186","DOI":"10.1109\/TITS.2020.2982186"},{"issue":"9","key":"1779_CR28","first-page":"1011","volume":"21","author":"BZ Qiu","year":"2006","unstructured":"B.Z. Qiu, J.Y. Shen, Grid-based and extend-based clustering algorithm for multi-density. Control Decision 21(9), 1011\u20131014 (2006)","journal-title":"Control Decision"},{"issue":"3","key":"1779_CR29","first-page":"77","volume":"25","author":"BZ Qiu","year":"2008","unstructured":"B.Z. Qiu, T. Yu, Boundary points detecting based gradient of grid. Microelectron Comput 25(3), 77\u201380 (2008)","journal-title":"Microelectron Comput"},{"key":"1779_CR30","first-page":"761","volume-title":"BRIM: An Efficient Boundary Points Detecting Algorithm. Advances in Knowledge Discovery and Data Mining","author":"BZ Qiu","year":"2007","unstructured":"B.Z. Qiu, F. Yue, J.Y. Shen, BRIM: An Efficient Boundary Points Detecting Algorithm. Advances in Knowledge Discovery and Data Mining (Springer, Berlin, 2007), pp. 761\u2013768"},{"key":"1779_CR31","first-page":"1246","volume-title":"The 7th International Conference on Computational Intelligence and Security","author":"BZ Qiu","year":"2011","unstructured":"B.Z. Qiu, S. Wang, in The 7th International Conference on Computational Intelligence and Security. A boundary detection algorithm of clusters based on dual threshold segmentation (IEEE, Sanya, 2011), pp. 1246\u20131250"},{"issue":"1","key":"1779_CR32","first-page":"171","volume":"30","author":"X Li","year":"2015","unstructured":"X. Li, P. Geng, B.Z. Qiu, Clustering boundary points detection technology for attribute data set. Control Decision 30(1), 171\u2013175 (2015)","journal-title":"Control Decision"}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01779-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13638-020-01779-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01779-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T08:47:36Z","timestamp":1630658856000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-020-01779-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,3]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1779"],"URL":"https:\/\/doi.org\/10.1186\/s13638-020-01779-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-21168\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-21168\/v1","asserted-by":"object"}]},"ISSN":["1687-1499"],"issn-type":[{"value":"1687-1499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,3]]},"assertion":[{"value":"1 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"We declare that there is no conflict of interest regarding this submission.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"168"}}