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Titlebook: Data Mining; 20th Australasian Co Laurence A. F. Park,Heitor Murilo Gomes,Simeon Sim Conference proceedings 2022 The Editor(s) (if applicab

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书目名称Data Mining
副标题20th Australasian Co
编辑Laurence A. F. Park,Heitor Murilo Gomes,Simeon Sim
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Data Mining; 20th Australasian Co Laurence A. F. Park,Heitor Murilo Gomes,Simeon Sim Conference proceedings 2022 The Editor(s) (if applicab
描述This book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. .The 17 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as ​research track and application track. .
出版日期Conference proceedings 2022
关键词artificial intelligence; classification; computational linguistics; computer networks; computer security
版次1
doihttps://doi.org/10.1007/978-981-19-8746-5
isbn_softcover978-981-19-8745-8
isbn_ebook978-981-19-8746-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Jim Duffy,Peter Koudal,Stephen Prattstructured query language before the rule-based formal query constructions. Three rules are exploited to generate a valid structured formal query. The web-based interactive application is developed to help maintainers access industrial maintenance knowledge graph which is constructed from text data.
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Hierarchical Topic Model Inference by Community Discovery on Word Co-occurrence NetworksCommunity Topic uncovers a topic hierarchy with a more coherent structure and a tighter relationship between parent and child topics. Community Topic is able to find this hierarchy more quickly and allows for on-demand sub- and super-topic discovery, facilitating corpus exploration by researchers.
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Qingliang Zhang,Xinyue Shu,Quanwang Wu content preservation. To investigate this we compared the average similarity of multiple styles of text from the Corpus of Diverse Styles using a variety of sentence embedding methods and find that those embeddings which are created from aggregated word embeddings are style invariant, but those created by sentence embeddings are not.
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T. A. Bhamra,J. Heeley,D. Tyleressing this complexity. Cluster definitions of Modal, Specialised and Aberrant are introduced as a descriptive framework on which to interpret said feature decomposition and to aid threshold setting. This strategy furthers the work introduced in [.] as refinement to an existing anomaly detection scheme.
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