Web Data Mining | SpringerLinkSecond Edition First Edition. Web mining aims to discover useful knowledge from Web hyperlinks, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining , Web content mining and Web usage mining. This book consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling , search , social network analysis , structured data extraction , information integration , opinion mining and sentiment analysis , Web usage mining , query log mining , computational advertising , and recommender systems are all treated in breadth and in depth the SVD matrix factorization algorithm of Simon Funk used in Netflix Prize Contest is described in detail.
Web Data Mining
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices.
It seems that you're in Germany. We have a dedicated site for Germany. Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data.
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