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
开阔眼界非常好 本科的基础不扎实的建议skip这本书吧 Data Mining 可是硕士博士们做的事情
评分应该说这部书可以把人引进门,但看了之后,总觉得还有些概念模糊之处,比如说数据挖掘的理论来源是什么?如何把这些算法从本质上分类? 我觉得,这方面,《实用数据挖掘》会更好些。另外,如何使用简单的软件,为企业或政府部门实现一个简单可见的数据挖掘呢?这方面,我只读...
评分//2017-05-20 13:30 这篇文章我已经欠了至少一年了,周五写记录时,本想写开始认真搞黑客,但突然发现之前的总结少这篇,心里实在过不去,遂补上,顺便梳理一下之前的学习总结,也了却一心愿。 数据挖掘的目标是从数据集中识别出一种或多种模式,并用所发现的模式进行分析或...
评分开阔眼界非常好 本科的基础不扎实的建议skip这本书吧 Data Mining 可是硕士博士们做的事情
评分简单来说几句吧。 很高兴看到这本书的作者之一Jiawei Han是中国人,先自豪一下。这本书最大的特点就是概念性强(相对于http://book.douban.com/subject/1820179/,《数据挖掘中的实用机器学习工具及技术》),从数据仓库到关联规则,从聚类到神经网络,最后几个章节还有数据挖...
good textbook, even though i decided not to follow the path towards a trendy so-called data scientist.
评分重温下Jiawei Han的这本经典数据挖掘教材
评分: TP311.13 /H233/3rd ed.
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jiawei是个好同志
本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度,google,bing,sogou 等
© 2025 book.quotespace.org All Rights Reserved. 小美书屋 版权所有