Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, Pakdd 2019, Macau, China, April 14-17, 2019, Proceedings, Part II
暫譯: 知識發現與數據挖掘的進展:第23屆亞太會議,Pakdd 2019,澳門,中國,2019年4月14-17日,會議論文集,第二部分

Yang, Qiang, Zhou, Zhi-Hua, Gong, Zhiguo

  • 出版商: Springer
  • 出版日期: 2019-03-23
  • 售價: $3,720
  • 貴賓價: 9.5$3,534
  • 語言: 英文
  • 頁數: 631
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030161447
  • ISBN-13: 9783030161446
  • 相關分類: Data-mining
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019.

The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.

商品描述(中文翻譯)

三卷本的 LNAI 11439、11440 和 11441 是第 23 屆亞太知識發現與數據挖掘會議(PAKDD 2019)經過徹底審稿的會議論文集,該會議於 2019 年 4 月在中國澳門舉行。

所呈現的 137 篇完整論文是從 542 篇投稿中仔細審核和選出的。這些論文展示了來自所有 KDD 相關領域的新想法、原創研究成果和實際開發經驗,包括數據挖掘、數據倉儲、機器學習、人工智慧、數據庫、統計學、知識工程、可視化、決策系統以及新興應用。論文被組織在以下主題部分:分類與監督學習;文本與意見挖掘;時空與流數據挖掘;因子與張量分析;醫療保健、生物資訊學及相關主題;聚類與異常檢測;深度學習模型與應用;序列模式挖掘;弱監督學習;推薦系統;社交網絡與圖挖掘;數據預處理與特徵選擇;表示學習與嵌入;挖掘非結構化與半結構化數據;行為數據挖掘;視覺數據挖掘;以及知識圖譜與可解釋的數據挖掘。

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