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Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. * Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects * Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods * Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
eBook
How Information Management Capability Influences Firm Performance
How do information technology capabilities contribute to firm performance? This study develops a conceptual model linking IT-enabled information management capability with three important organizational capabilities (customer management capability, process management capability, and performance management capability). We argue that these three capabilities mediate the relationship between information management capability and firm performance. We use a rare archival data set from a conglomerate business group that had adopted a model of performance excellence for organizational transformation based on the Baldrige criteria. This data set contains actual scores from high quality assessments of firms and intraorganizational units of the conglomerate, and hence provides unobtrusive measures of the key constructs to validate our conceptual model. We find that information management capability plays an important role in developing other firm capabilities for customer management, process management, and performance management. In turn, these capabilities favorably influence customer, financial, human resources, and organizational effectiveness measures of firm performance. Among key managerial implications, senior leaders must focus on creating necessary conditions for developing IT infrastructure and information management capability because they play a foundational role in building other capabilities for improved firm performance. The Baldrige model also needs some changes to more explicitly acknowledge the role and importance of information management capability so that senior leaders know where to begin in their journey toward business excellence.
Journal Article
High-Level Information Fusion Management and Systems Design
High-level information fusion is the ability of a fusion system to capture awareness and complex relations, reason over past and future events, utilize direct sensing exploitations and tacit reports, and discern the usefulness and intention of results to meet system-level goals. This authoritative book serves a practical reference for developers, designers, and users of data fusion services that must relate the most recent theory to real-world applications. This unique volume provides alternative methods to represent and model various situations and describes design component implementations of fusion systems. Designers find expert guidance in applying current theories, selecting algorithms and software components, and measuring expected performance of high-level fusion systems.
eBook
Managing Information Services
This third edition of Jo Bryson's highly regarded Managing Information Services has been thoroughly revised with an emphasis on managing for a sustainable future. Libraries and information services face uncertain times and this new edition tackles the challenges of planning and managing change, future-proofing for tomorrow, and leading the transformation to a sustainable future. The text also addresses the integration of information services including librarianship, records management and ICT. Essential reading for information students, this text also serves as a comprehensive and detailed reference on the key management topics for information service managers.
eBook
Big Data, Little Data, No Data
\"Big Data\" is on the covers of Science, Nature , the Economist , and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
eBook