This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.

Thisyear'svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web's p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

This book constitutes the thoroughly refereed post-proceedings of the 8th International Workshop on Mining Web Data, WEBKDD 2006, held in Philadelphia, PA, USA in August 2006 in conjunction with the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006.

The 13 revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were carefully selected for inclusion in the book. The enhanced papers show new technologies from areas like adaptive mining methods, stream mining algorithms, techniques for the Grid, especially flat texts, documents, pictures and streams, usability, e-commerce applications, personalization, and recommendation engines.

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 nine revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were carefully selected for inclusion in the book.

This book constitutes the thoroughly refereed post-proceedings of the 6th International Workshop on Mining Web Data, WEBKDD 2004, held in Seattle, WA, USA in August 2004 in conjunction with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004.

The 11 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 extended papers are subdivided into 4 general groups: Web usage analysis and user modeling, Web personalization and recommender systems, search personalization, and semantic Web mining. The latter contains also papers from the joint KDD workshop on Mining for and from the Semantic Web, MSW 2004.

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.