{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:59:59Z","timestamp":1761897599404},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p> Gradual emerging patterns (GEPs) are gradual item sets that occur less frequently in one data set and more frequently in another. For instance, let \u2018fan speed\u2019 and \u2018temperature\u2019 be attributes of two numerical data sets. A gradual item set \u201cthe higher the speed, the lower the temperature\u201d (which correlates a data set\u2019s attributes) becomes a GEP if it is less frequent (in terms of support as in frequent pattern mining) in one data set and more frequent in another. However, such patterns do not indicate how time gap impacts the emergence. Many correlations appear over time, for instance when phenomena appear after some meteorological situation due to latency. Previous works have not taken this temporal aspect into account. In this paper, we introduce temporal gradual emerging patterns (TGEPs) which are temporal gradual patterns (TGPs) whose frequency supports increase significantly between transformed data sets. For instance, a TGP \u201cthe higher the speed, the lower the temperature, almost 3 minutes later\u201d becomes a TGEP if it occurs more frequently in one transformed data set than in another. Furthermore, we extend border manipulation to the case of mining TGEPs. In addition, we propose a more efficient ant colony optimization technique that exploits a heuristic approach to construct TGEPs. <\/jats:p>","DOI":"10.1142\/s0218488521500288","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T01:00:15Z","timestamp":1631235615000},"page":"655-676","source":"Crossref","is-referenced-by-count":4,"title":["Mining Fuzzy Temporal Gradual Emerging Patterns"],"prefix":"10.1142","volume":"29","author":[{"given":"Dickson Odhiambo","family":"Owuor","sequence":"first","affiliation":[{"name":"School of Computing and Engineering Sciences, Strathmore University, Nairobi, Kenya"}]},{"given":"Anne","family":"Laurent","sequence":"additional","affiliation":[{"name":"LIRMM, University of Montpellier, CNRS, Montpellier, France"}]},{"given":"Joseph Onderi","family":"Orero","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering Sciences, Strathmore University, Nairobi, Kenya"}]}],"member":"219","published-online":{"date-parts":[[2021,10,13]]},"container-title":["International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218488521500288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T19:03:27Z","timestamp":1634583807000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218488521500288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":0,"journal-issue":{"issue":"05","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10.1142\/S0218488521500288"],"URL":"https:\/\/doi.org\/10.1142\/s0218488521500288","relation":{},"ISSN":["0218-4885","1793-6411"],"issn-type":[{"value":"0218-4885","type":"print"},{"value":"1793-6411","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10]]}}}