數據挖掘作為一個新興的多學科交叉應用領域,正在各行各業的決策支持活動中扮演著越來越重要的角色.本書將介紹數據挖掘(Data Mining)(Knowledge Discovery from Databases)的基本知識,以及從大量有噪聲,不完整,甚至是不一致數據集合中,挖掘聘用意義的模式知識所涉及的概念與技術方法.
The rapid growth of the Web in the past two decades has made it the larg-
est publicly accessible data source in the world. Web Mining aims to dis-
cover useful information or knowledge from Web hyperlinks, page con-
tents, and usage logs. Based on the primary kinds of data used in the
Mining process, Web Mining tasks can be categorized into three main
types: Web structure Mining, Web content Mining and Web usage Mining.
The ROSETTA C++ library is a collection of C++ classes and routines that enable discernibility-based empirical modelling and data Mining, developed as part of my dissertation. A brief presentation of the library can be found therein.
