Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Shifeng Ren"'
Autor:
Ying Li, Jie Cui, Lei Liu, William S. Hambright, Yutai Gan, Yajun Zhang, Shifeng Ren, Xianlin Yue, Liwei Shao, Yan Cui, Johnny Huard, Yanling Mu, Qingqiang Yao, Xiaodong Mu
Publikováno v:
Cell Death and Disease, Vol 15, Iss 7, Pp 1-12 (2024)
Abstract The mechanism regulating cellular senescence of postmitotic muscle cells is still unknown. cGAS-STING innate immune signaling was found to mediate cellular senescence in various types of cells, including postmitotic neuron cells, which howev
Externí odkaz:
https://doaj.org/article/ffa985da1b7e470a84d3ef14ac735e81
Publikováno v:
IEEE Access, Vol 6, Pp 7593-7609 (2018)
High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous st
Externí odkaz:
https://doaj.org/article/0e42cc5f2aae40f3afd7bcab32a0bfa2
Publikováno v:
IEEE Access, Vol 5, Pp 12927-12940 (2017)
High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns. High average-utility itemset mining (
Externí odkaz:
https://doaj.org/article/4235a19bffb847358816389eaf2ac536
Publikováno v:
Engineering Applications of Artificial Intelligence. 72:136-149
High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of
Publikováno v:
IEEE Access, Vol 6, Pp 7593-7609 (2018)
High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous st
Autor:
Bay Vo, Shifeng Ren, Tzung-Pei Hong, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Ja-Hwung Su
Publikováno v:
Applied Intelligence. 47:331-346
Mining high-utility itemsets (HUIs) in transactional databases has become a very popular research topic in recent years. A popular variation of the problem of HUI mining is to discover high average-utility itemsets (HAUIs), where an alternative measu
Publikováno v:
IEEE Access, Vol 5, Pp 12927-12940 (2017)
High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns. High average-utility itemset mining (
Publikováno v:
Advances in Intelligent Information Hiding and Multimedia Signal Processing ISBN: 9783319638553
IIH-MSP (1)
IIH-MSP (1)
In this paper, we propose an efficient algorithm to discover HAUIs based on the compact average-utility list structure. A tighter upper-bound model is used to instead of the traditional auub model used in HAUIM to lower the upper-bound value. Three p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79e632262cfcd243906c48444f6a2ddf
https://doi.org/10.1007/978-3-319-63856-0_25
https://doi.org/10.1007/978-3-319-63856-0_25
Publikováno v:
Proceedings of the 4th Multidisciplinary International Social Networks Conference.
In this paper, we presented a tighter upper-bound model to instead of the traditional auub model for mining the HAUIs. A modified average-utility-list structure is also designed to keep the necessary information for later mining process, thus reducin
Publikováno v:
Advances in Intelligent Information Hiding and Multimedia Signal Processing ISBN: 9783319502113
In this paper, an efficient algorithm with three pruning strategies are presented to provide tighter upper-bound average-utility of the itemsets, thus reducing the search space for mining the set of high average-utility itemsets (HAUIs). The first st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20c8f7b963d6ca6e01c7218bef2527fb
https://doi.org/10.1007/978-3-319-50212-0_13
https://doi.org/10.1007/978-3-319-50212-0_13