Tree-based Algorithms for Incremental, Utility, and Fuzzy Data Mining - Lin, Jerry Chun-Wei
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In the first part of this book, three Pre-FUFP maintenance algorithms are thus proposed to efficiently maintain and update the FUFP-tree structures regardless of whether records are inserted, deleted or modified in dynamic databases. In the second part of this book, a novel HUP-tree algorithm is proposed to efficiently mine the high utility itemsets based on the downward closure property. A HUP tree is first designed to keep the related information for later mining process. A HUP-growth mining algorithm is then presented to efficiently mine high utility itemsets from it. In the third part of…mehr

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Produktbeschreibung
In the first part of this book, three Pre-FUFP maintenance algorithms are thus proposed to efficiently maintain and update the FUFP-tree structures regardless of whether records are inserted, deleted or modified in dynamic databases. In the second part of this book, a novel HUP-tree algorithm is proposed to efficiently mine the high utility itemsets based on the downward closure property. A HUP tree is first designed to keep the related information for later mining process. A HUP-growth mining algorithm is then presented to efficiently mine high utility itemsets from it. In the third part of this book, we attempt to extend the FP-tree algorithm for handling quantitative data from the global values of fuzzy regions. Thus, the fuzzy FP-tree algorithm, the compressed fuzzy frequent pattern tree (CFFP-tree) algorithm, and the upper-bound fuzzy frequent pattern tree (UBFFP-tree) algorithm are then proposed to efficiently mine the fuzzy frequent itemsets.
Autorenporträt
Jerry Chun-Wei Lin is currently an Assistant Professor at School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China. His research interests include data mining, machine learning, artificial intelligence, social computing, and privacy-preserving and security technologies.