Advances in knowledge discovery in databases [electronic resource]

• 作者： Adhikari, Animesh.
• 其他作者：
  • Adhikari, Jhimli.
  • SpringerLink (Online service)
• 其他題名：
  • Intelligent systems reference library,
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Intelligent systems reference library,v.79
• 主題： Data mining , Engineering , Computational intelligence , Data Mining and Knowledge Discovery. , Artificial Intelligence (incl. Robotics)
• ISBN： 9783319132129 (electronic bk.) 、 9783319132112 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Introduction -- Synthesizing conditional patterns in a database -- Synthesizing arbitrary Boolean expressions induced by frequent itemsets -- Measuring association among items in a database -- Mining association rules induced by item and quantity purchased -- Mining patterns different related databases -- Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources -- Clustering items in time-stamped databases -- Synthesizing some extreme association rules from multiple databases -- Clustering local frequency items in multiple data sources -- Mining patterns of select items in different data sources -- Mining calendar-based periodic patterns in time-stamped data -- Measuring influence of an item in time-stamped databases -- Clustering multiple databases induced by local patterns -- Enhancing quality of patterns in multiple related databases -- Concluding remarks.
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• 摘要註: This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.
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