{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:53:37Z","timestamp":1773888817089,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.<\/jats:p>","DOI":"10.1002\/ima.22533","type":"journal-article","created":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T14:30:00Z","timestamp":1608388200000},"page":"1265-1279","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mining frequent approximate patterns in large networks"],"prefix":"10.1002","volume":"31","author":[{"given":"Kaouthar","family":"Driss","sequence":"first","affiliation":[{"name":"RIADI Laboratory, National School of Computer Sciences University of Manouba  Manouba Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"RIADI Laboratory, National School of Computer Sciences University of Manouba  Manouba Tunisia"},{"name":"IS Department, College of Computer Science and Engineering Taibah University  Medina Saudi Arabia"}]},{"given":"Aur\u00e9lie","family":"Leborgne","sequence":"additional","affiliation":[{"name":"ICube Laboratory University of Strasbourg  Strasbourg France"}]},{"given":"Pierre","family":"Gan\u00e7arski","sequence":"additional","affiliation":[{"name":"ICube Laboratory University of Strasbourg  Strasbourg France"}]}],"member":"311","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0059-1"},{"key":"e_1_2_11_3_1","first-page":"1","article-title":"Event detection through differential pattern mining in cyber\u2010physical systems","author":"Bhuiyan MZA","year":"2017","journal-title":"IEEE Trans Big Data"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2894972"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.09.013"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9629-z"},{"key":"e_1_2_11_7_1","first-page":"1","article-title":"Frequent itemset mining: a 25\u2009years review","volume":"9","author":"Luna JM","year":"2019","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.03.030"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.09.018"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.12.018"},{"issue":"1","key":"e_1_2_11_11_1","first-page":"385","article-title":"AGraP: an algorithm for mining frequent patterns in a single graph using inexact matching","volume":"44","author":"Flores\u2010Garrido M","year":"2014","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"e_1_2_11_12_1","first-page":"1433","article-title":"An efficient approach for accurate frequent pattern mining practising threshold values","volume":"4","author":"Thakkar J","year":"2018","journal-title":"Int J Sci Res Sci Eng Technol"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001418600121"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22346"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13637-017-0059-z"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2018.0171"},{"key":"e_1_2_11_17_1","first-page":"1","article-title":"Frequent pattern mining in multidimensional organizational networks","volume":"2018","author":"Gad\u00e1r L","year":"2019","journal-title":"Sci Rep"},{"issue":"5","key":"e_1_2_11_18_1","first-page":"483","article-title":"A hybrid artificial immune network for detecting","volume":"97","author":"Karimi\u2010Majd AM","year":"2014","journal-title":"Comput Secur"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12273"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2803818"},{"key":"e_1_2_11_21_1","article-title":"CD\u2010SPM: cross\u2010domain book recommendation using sequential pattern mining and rule mining","author":"Anwar T","year":"2019","journal-title":"J King Saud Univ \u2010 Comput Inform Sci"},{"key":"e_1_2_11_22_1","volume-title":"Efficient Pattern Mining of Big Data Using Graphs","author":"Bhatia V","year":"2018"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.06.054"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22406"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-018-0181-9"},{"key":"e_1_2_11_26_1","doi-asserted-by":"crossref","unstructured":"DrissK BoulilaW BatoolA AhmadJ.A Novel Approach for Classifying Diabetes Patients Based on Imputation and Machine Learning. UK\/China Emerging Technologies (UCET2020). 1\u20104;2020.","DOI":"10.1109\/UCET51115.2020.9205378"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939535"},{"key":"e_1_2_11_28_1","first-page":"2290","article-title":"A survey on extracting frequent subgraphs","author":"Thomas S","year":"2016","journal-title":"IEEE Xplore"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.10.023"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/2794367.2794370"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12293-020-00300-x"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2896267"},{"issue":"4","key":"e_1_2_11_33_1","first-page":"782","article-title":"An optimized overlapping and disjoint community detection techniques using improved community overlap propagation algorithm in complex networks","volume":"7","author":"Saradha CS","year":"2020","journal-title":"J Crit Rev"},{"key":"e_1_2_11_34_1","first-page":"445","article-title":"gApprox: mining frequent approximate patterns from a massive network","author":"Chen C","year":"2007","journal-title":"Proc \u2010 IEEE Int Conf Data Mining, ICDM"},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3007485"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2018.0171"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.09.018"},{"issue":"1","key":"e_1_2_11_38_1","first-page":"118","article-title":"Overlapping community detection with a novel hybrid metaheuristic optimisation algorithm","volume":"12","author":"Messaoudi I","year":"2020","journal-title":"Int J Data Min Model Manag"},{"key":"e_1_2_11_39_1","first-page":"1","article-title":"Frequent subgraph mining algorithms for single large graphs\u2014a brief survey","author":"Dhiman A","year":"2016","journal-title":"IEEE Xplore"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01412-9"},{"key":"e_1_2_11_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07566-2_23"},{"key":"e_1_2_11_42_1","doi-asserted-by":"crossref","unstructured":"ChenH YanX LiuM YanD ZhaoY ChengJ.G\u2010Miner: An Efficient Task\u2010Oriented Graph Mining System. InProceedings of the 13th EuroSys Conference EuroSys 2018. 1\u201012;2018.","DOI":"10.1145\/3190508.3190545"},{"issue":"8","key":"e_1_2_11_43_1","first-page":"997","article-title":"Mining frequent patterns in web log data using Gaston algorithm","volume":"6","author":"Lakshmi NJ","year":"2017","journal-title":"Int J Eng Sci"},{"key":"e_1_2_11_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947855"},{"key":"e_1_2_11_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-018-00376-7"},{"key":"e_1_2_11_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100303"},{"key":"e_1_2_11_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100318"},{"key":"e_1_2_11_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2016.11.006"},{"key":"e_1_2_11_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2017.10.006"},{"key":"e_1_2_11_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1102-9"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22533","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ima.22533","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T16:33:52Z","timestamp":1693326832000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.22533"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,19]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["10.1002\/ima.22533"],"URL":"https:\/\/doi.org\/10.1002\/ima.22533","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,19]]},"assertion":[{"value":"2020-09-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-30","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}