Search (8 results, page 1 of 1)

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Abstract

This book's purpose is to teach people who will be searching or designing text retrieval systems how the systems work. For designers, it covers problems they will face and reviews currently available solutions to provide a basis for more advanced study. For the searcher its purpose is to describe why such systems work as they do. Text Information Retrieval Systems, Second Edition is primarily about computer-based retrieval systems, but the principles apply to non-mechanized ones as well. - Winner of the ASIS Best Information Science Book Award 2000!

LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

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Date

23. 3.2008 19:10:22

Footnote

In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.

LCSH

Knowledge acquisition (Expert systems)

Series

Morgan Kaufmann series in data management systems

Subject

Knowledge acquisition (Expert systems)

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Abstract

Information or Document Retrieval is the subject of this book. It is not an introductory book, although it is self-contained in the sense that it is not necessary to have a background in the theory or practice of Information Retrieval in order to understand its arguments. The book presents, as clearly as possible, one particular perspective on Information Retrieval, and attempts to say that certain aspects of the theory or practice of the management of documents are more important than others. The majority of Information Retrieval research has been aimed at the more experimentally tractable small-scale systems, and although much of that work has added greatly to our understanding of Information Retrieval it is becoming increasingly apparent that retrieval systems with large data bases of documents are a fundamentally different genre of systems than small-scale systems. If this is so, which is the thesis of this book, then we must now study large information retrieval systems with the same rigor and intensity that we once studied small-scale systems. Hegel observed that the quantitative growth of any system caused qualitative changes to take place in its structure and processes.

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Abstract

This book provides relevant content in the areas of information retrieval systems, services, and research; covering topics such as social tagging, collaborative querying, social network analysis, subjective relevance judgments, and collaborative filtering. Answering the increasing demand for authoritative resources on Internet technologies, this will make an indispensable addition to any library collection

Content

Inhalt Collaborating to search effectively in different searcher modes through cues and specialty search / Naresh Kumar Agarwal and Danny C.C. Poo -- Collaborative querying using a hybrid content and results-based approach / Chandrani Sinha Ray ... [et al.] -- Collaborative classification for group-oriented organization of search results / Keiichi Nakata and Amrish Singh -- A case study of use-centered descriptions : archival descriptions of what can be done with a collection / Richard Butterworth -- Metadata for social recommendations : storing, sharing, and reusing evaluations of learning resources / Riina Vuorikari, Nikos Manouselis, and Erik Duval -- Social network models for enhancing reference-based search engine rankings / Nikolaos Korfiatis ... [et al.] -- From PageRank to social rank : authority-based retrieval in social information spaces / Sebastian Marius Kirsch ... [et al.] -- Adaptive peer-to-peer social networks for distributed content-based Web search / Le-Shin Wu ... [et al.] -- The ethics of social information retrieval / Brendan Luyt and Chu Keong Lee -- The social context of knowledge / Daniel Memmi -- Social information seeking in digital libraries / George Buchanan and Annika Hinze -- Relevant intra-actions in networked environments / Theresa Dirndorfer Anderson -- Publication and citation analysis as a tool for information retrieval / Ronald Rousseau -- Personalized information retrieval in a semantic-based learning environment / Antonella Carbonaro and Rodolfo Ferrini -- Multi-agent tourism system (MATS) / Soe Yu Maw and Myo-Myo Naing -- Hybrid recommendation systems : a case study on the movies domain / Konstantinos Markellos ... [et al.].

LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

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LCSH

Expert systems (Computer science)

Series

International handbook on information systems

Subject

Expert systems (Computer science)

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Series

Advances in information systems and management science; 10

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Series

Advances in information systems and management science; 10

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LCSH

Knowledge acquisition (Expert systems)

Subject

Knowledge acquisition (Expert systems)