Web数据挖掘与Web使用分析进展/Advances in web mining and web usage analysis

This book constitutes the thoroughly refereed post-proceedings of the 7th International Workshop on Mining Web Data, WEBKDD 2005, held in Chicago, IL, USA in August 2005 in conjunction with the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005.
The 9 revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were carfully selected for inclusion in the book. The enhanced papers show that Web mining techniques and applications have to more effectively integrate a variety of types of data across multiple channels and from different sources in addition to usage, such as content, structure, and semantics. Thus a next generation of intelligent applications is stimulated for more effective exploitation and mining of multi-faceted data. The papers express also the need to study and design robust recommender systems that can resist various malicious manipulations.

数据挖掘Clementine应用实务	数据挖掘算法与应用	金融数据挖掘	数据挖掘原理	空间数据挖掘理论与应用
数据挖掘导论——图灵计算机科学丛书	数据挖掘：概念与技术（英文版·第2版）	数据挖掘原理与算法（第二版）	数据挖掘原理与算法	生物医学数据挖掘

The LNAI series reports state-of-the-art results in artificial intelligence re-search, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelli- gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes.
proceedings (published in time for the respective conference)；
post-proceedings (consisting of thoroughly revised final full papers)；
research monographs (which may be based on PhD work).

Mining Significant Usage Patterns from Clickstream Data
Using and Learning Semantics in Frequent Subgraph Mining
Overcoming Incomplete User Models in Recommendation Systems Via an Ontology
Data Sparsity Issues in the Collaborative Filtering Framework
USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments
Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation
Adaptive Web Usage Profiling
On Clustering Techniques for Change Diagnosis in Data Streams
Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks
Author Index