Hanghang Tong – författare

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets.

After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networksAddresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and societyProvides a comprehensive, practical look at the concepts and techniques needed to get the most out of your dataVisit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata

Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.

This SpringerBrief presents a typical life-cycle of mobile data mining applications, including:data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensorsfeature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed datamodel and algorithm designIn particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-timeEnergy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors  explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization.  Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency.This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide.

This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions.

Moreover, the complexity of the connectivity would predominantly affect the hardness of connectivity optimization, which is a fundamental problem for network connectivity studies.