Jiawei Han(韩家炜),是伊利诺伊大学厄巴纳-尚佩恩分校计算机科学系的Bliss教授。他因知识发现和数据挖掘研究方面的贡献而获得许多奖励,包括ACM SIGKDD创新奖(2004)、IEEE计算机学会技术成就奖(2005)和IEEE W.Wallace McDowell奖(2009)。他是ACM和IEEE会士。他还担任《ACM Transactions on Knowledge Discovery from Data》的执行主编(2006—2011)和许多杂志的编委,包括《IEEE Transactions on Knowledge and Data Engineering》和《Data Mining Knowledge Discovery》。
拥有加拿大康考迪亚大学计算机科学硕士学位,现在加拿大西蒙弗雷泽大学从事博士后研究工作。
The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.
* Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
这是一本从数据库角度阐述数据挖掘的书,主要关注从商业数据库的大量事务数据中寻找有用信息的各种方法。数据库和大数据是贯穿全书的核心。 全书大致可以分成两部分。前一部分重点是数据仓库的构建以及在此过程中的数据整合与化简,对于数据库的设计与数据整理很有启发...
评分作者是FP-Growth的发明人之一,本身实力不弱。但看了国内外的一些评论后,觉得此书偏向文献综述的类型,适合当作参考手册。 亚马逊地址: http://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790/ref=cm_rdp_product
评分这本书被翻译的佶屈聱牙,除了给学习数据挖掘的人增添负担,什么积极的作用的没有。 不知道有多少人因为这本不通的书而失去对数据挖掘的兴趣。 教授真的是毁人不倦啊,各种官方语言,妈的是要当官吗?
评分我了个擦 , 连个非限制性定语从句都翻译不了,你翻译毛啊。还不如看原版。你们两个真是叫兽啊。本来都不屑去骂,但是连个定于从句都搞不通顺,叫兽你就这水平?你让研究生替你翻译的话,你研究生的水平也不至于如此奇差吧,还没过四级呢吧。不评很差是看在原著的面子上。
jiawei是个好同志
评分推荐和Coursera的专项课程一起听。Coursera的Slides给出了书中很多较为简略环节的参考文献,书和课程组合,兼顾基础与引申。在线课程精心准备,游戏化做得非常棒,习题集还搞了个名人堂机制,动力满满啊!论坛也很活跃,负责算法R实现那个TA尤其赞,学到了很多!
评分重温下Jiawei Han的这本经典数据挖掘教材
评分推荐和Coursera的专项课程一起听。Coursera的Slides给出了书中很多较为简略环节的参考文献,书和课程组合,兼顾基础与引申。在线课程精心准备,游戏化做得非常棒,习题集还搞了个名人堂机制,动力满满啊!论坛也很活跃,负责算法R实现那个TA尤其赞,学到了很多!
评分推荐和Coursera的专项课程一起听。Coursera的Slides给出了书中很多较为简略环节的参考文献,书和课程组合,兼顾基础与引申。在线课程精心准备,游戏化做得非常棒,习题集还搞了个名人堂机制,动力满满啊!论坛也很活跃,负责算法R实现那个TA尤其赞,学到了很多!
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