"This book addresses existing solutions for data mining, with particular emphasis on potential real-world applications. It captures defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining"--Provided by publisher.
This volume introduces data mining through case studies of enrollment management. Six case studies employed data mining for solving real-life issues in enrollment yield, retention, transfer-outs, utilization of advanced-placement scores, and predicting graduation rates, among others. The authors furnish a tangible sense of data mining at work. The volume also demonstrates that data mining bears great potential to enhance institutional research. The opening chapter deciphers the similarities and differences between data mining and statistics, debunks the myths surrounding both data mining and traditional statistics, and points out the intrinsic conflict between statistical inference and the emerging need for individual pattern recognition and resulting customized treatment of students - the so-called new reality in applied institutional research. This is the 131st volume of New Directions for Institutional Research, a quarterly journal published by Jossey-Bass. Click here to see the entire list of titles for New Directions for Institutional Research.
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
As long as there have been U. S. colleges and universities, there have been entry courses that pose difficulties for students – courses that have served more as weeding-out rather than gearing-up experiences for undergraduates. This volume makes the case that the weed-out dynamic is no longer acceptable – if it ever was. Contemporary postsecondary education is characterized by vastly expanded access for historically underserved populations of students, and this new level of access is coupled withincreased scrutiny of retention and graduation outcomes. Chapters in this volume define and explore issues in gateway courses and provide various examples of how to improve teaching, learning and outcomes in these foundational components of the undergraduateexperience. This is the 180th volume of the Jossey-Bass quarterly report series New Directions for Higher Education. Addressed to presidents, vice presidents, deans, and other higher education decision makers on all kinds of campuses, it provides timely information and authoritative advice about major issues and administrative problems confronting every institution.
This book is an authoritative collection of contributions in the field of soft-computing. Based on selected works presented at the 6th World Conference on Soft Computing, held on May 22-25, 2016, in Berkeley, USA, it describes new theoretical advances, as well as cutting-edge methods and applications. Theories cover a wealth of topics, such as fuzzy logic, cognitive modeling, Bayesian and probabilistic methods, multi-criteria decision making, utility theory, approximate reasoning, human-centric computing and many others. Applications concerns a number of fields, such as internet and semantic web, social networks and trust, control and robotics, computer vision, medicine and bioinformatics, as well as finance, security and e-Commerce, among others. Dedicated to the 50th Anniversary of Fuzzy Logic and to the 95th Birthday Anniversary of Lotfi A. Zadeh, the book not only offers a timely view on the field, yet it also discusses thought-provoking developments and challenges, thus fostering new research directions in the diverse areas of soft computing.
Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.
Editor John Schuh and his fellow contributors, all experts in the field, detail the methodological aspects of conducting assessment projects specifically for the student affairs practitioner who is ready to conduct assessment projects, but is not quite sure how to manage their technical aspects. Using a variety of case studies and concrete examples to illustrate various assessment approaches, the authors lead the reader step-by-step through each phase of the assessment process with jargon-free, hands-on guidance.
The introduction of endogenous growth theory has led to new interest in the role of the entrepreneur as an agent driving technical change at the local regional level. This book examines theoretical and methodological issues surrounding the interface of the entrepreneur in regional growth dynamics on the one hand and on the other presents illuminating case studies. In total the book's contributions amplify understanding of such critical issues as the relationship between innovation and entrepreneurship, the entrepreneur's role in transforming knowledge into something economically useful, and knowledge commercialization with both conceptual and empirical contributions. The emergence of endogenous growth theory has unleashed a flurry of new hypotheses and related inquiries that have in turn created an exciting dynamic in the conceptual, theoretical and empirical foundations of the field. A central feature has been the recognition that local initiatives matter in how regions grow and adjust to changes and shocks. Moreover, it is the role of technical change, driven by entrepreneurs, that motivates these initiatives. This volume begins by outlining and explaining the theory and method behind entrepreneurship and development. This is followed by specific case studies of practice and policy. These cases are region specific, offering the reader concrete, empirically based research results. Scholars and students in economics, entrepreneurship and public policy will find this volume a valuable tool in understanding the latest research in regional economic development.
Collaborations that integrate diverse perspectives are critical to addressing many of our complex scientific and societal problems. Yet those engaged in cross-disciplinary team science often face institutional barriers and collaborative challenges. Strategies for Team Science Success offers readers a comprehensive set of actionable strategies for reducing barriers and overcoming challenges and includes practical guidance for how to implement effective team science practices. More than 100 experts--including scientists, administrators, and funders from a wide range of disciplines and professions-- explain evidence-based principles, highlight state-of the-art strategies, tools, and resources, and share first-person accounts of how they’ve applied them in their own successful team science initiatives. While many examples draw from cross-disciplinary team science initiatives in the health domain, the handbook is designed to be useful across all areas of science. Strategies for Team Science Success will inspire and enable readers to embrace cross-disciplinary team science, by articulating its value for accelerating scientific progress, and by providing practical strategies for success. Scientists, administrators, funders, and others engaged in team science will also leave equipped to develop new policies and practices needed to keep pace in our rapidly changing scientific landscape. Scholars across the Science of Team Science (SciTS), management, organizational, behavioral and social sciences, public health, philosophy, and information technology, among other areas of scholarship, will find inspiration for new research directions to continue advancing cross-disciplinary team science.