Philip S. Yu

Jing He

Guangyan Huang

Yong Shi

Xingsen Li

Zhijun Xie

Jie Cao

Fu Xiao

This book constitutes the refereed proceedings of the 6th International Conference on Data Science, ICDS 2019, held in Ningbo, China, during May 2019. The 64 revised full papers presented were carefully reviewed and selected from 210 submissions. The research papers cover the areas of Advancement of Data Science and Smart City Applications, Theory of Data Science, Data Science of People and Health, Web of Data, Data Science of Trust and Internet of Things.

6th International Conference, ICDS 2019, Ningbo, China, May 15–20, 2019, Revised Selected Papers

Data Science

Chengqi Zhang

Peng Zhang

Wei Huang

Yingjie Tian

Yangyong Zhu

This book constitutes the refereed proceedings of the Second International Conference on Data Science, ICDS 2015, held in Sydney, Australia, in August 8-9, 2015. The 19 revised full papers and 5 short papers presented were carefully reviewed and selected from 31 submissions. The papers focus on the following topics: mathematical issues in data science; big data issues and applications; data quality and data preparation; data-driven scientific research; evaluation and measurement in data service; big data mining and knowledge management; case study of data science; social impacts of data science.

Second International Conference, ICDS 2015, Sydney, Australia, August 8-9, 2015, Proceedings

Rajeev Motwani

Jiawei Han

Vipin Kumar

Hillol Kargupta

Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Next Generation of Data Mining

Jiawei Zhang

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

An Application on Social Networks

Broad Learning Through Fusions

Longbing Cao

Andreas L. Symeonidis

Vladimir Gorodetsky

Munindar P. Singh

Yifeng Zeng

This book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on Agents and Data Mining Interaction, ADMI 2012, held in Valencia, Spain, in June 2012. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on agents for data mining, data mining for agents, and agent mining applications.

8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers

Agents and Data Mining Interaction

Xiao Wang

Chuan Shi

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.

Heterogeneous Graph Representation Learning and Applications

This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.

Heterogeneous Information Network Analysis and Applications

Christos Faloutsos

This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

Link Mining: Models, Algorithms, and Applications

Guandong Xu

Hiroshi Motoda

Wolfgang Nejdl

Jaideep Srivastava

Gang Li

Irwin King

Ee-peng Lim

Ya Zhang

This book constitutes the thoroughly refereed proceedings of the International Workshops on Behavior and Social Informatics and Computing, BSIC 2013, held as collocated event of IJCAI 2013, in Beijing, China in August 2013 and the International Workshop on Behavior and Social Informatics, BSI 2013, held as satellite workshop of PAKDD 2013, in Gold Coast, Australia, in April 2013. The 23 papers presented were carefully reviewed and selected from 58 submissions. The papers study a wide range of techniques and methods for behavior/social-oriented analyses including behavioral and social interaction and network, behavioral/social patterns, behavioral/social impacts, the formation of behavioral/social-oriented groups and collective intelligence and behavioral/social intelligence emergence.

International Workshop on Behavior and Social Informatics, BSI 2013, Gold Coast, Australia, April 14-17, and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Beijing, China, August 3-9, 2013, Revised Selected Papers

Behavior and Social Computing

Ana L.C. Bazzan

Gerhard Weiss

This book constitutes the thoroughly refereed post-workshop proceedings of the 7th International Workshop on Agents and Data Mining Interaction, ADMI 2011, held in Taipei, Taiwan, in May 2011 in conjunction with AAMAS 2011, the 10th International Joint Conference on Autonomous Agents and Multiagent Systems. The 11 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on agents for data mining; data mining for agents; and agent mining applications.

7th International Workshop, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers

Ming-Syan Cheng

Bing Liu

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).

6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings

Advances in Knowledge Discovery and Data Mining

Yanchang Zhao

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Domain Driven Data Mining

Philip S. Yu

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