Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data mining and data modeling are hot topics and are under fast development. Because of their wide applications and rich research contents, many practitioners and academics are attracted to work in these areas. With a view to promoting communication and collaboration among the practitioners and researchers in Hong Kong, a workshop on data mining and modeling was held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical Research, The University of Hong Kong, and Prof Tze Leung Lai (Stanford University), C V Starr Professor of the University of Hong Kong, initiated the work

Management Information System --- dataverwerking --- econometrie --- forecasting --- markov-processen --- regressie-analyse --- tijdreeksanalyse --- wiskundige statistiek --- Data mining --- Database searching. --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In the Information Society, information holds the master key to economic influence. Similarity Search: The Metric Space Approach will focus on efficient ways to locate user-relevant information in collections of objects, the similarity of which is quantified using a pairwise distance measure. This book is a direct response to recent advances in computing, communications and storage which have led to the current flood of digital libraries, data warehouses and the limitless heterogeneity of internet resources. Similarity Search: The Metric Space Approach will introduce state-of-the-art in developing index structures for searching complex data modeled as instances of a metric space. This book consists of two parts. Part 1 presents the metric search approach in a nutshell by defining the problem, describes major theoretical principals, and provides an extensive survey of specific techniques for a large range of applications. Part 2 concentrates on approaches particularly designed for searching in very large collections of data. Similarity Search: The Metric Space Approach is designed for a professional audience, composed of academic researchers as well as practitioners in industry. This book is also suitable as introductory material for graduate-level students in computer science.

Database searching. --- Data structures (Computer science) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Information retrieval --- Database searching --- Information storage and retrieval systems --- Online bibliographic searching --- Reference services (Libraries) --- -Reference services (Libraries) --- -Libraries --- Library reference services --- Reference work (Libraries) --- Information services --- Public services (Libraries) --- On-line bibliographic searching --- Electronic information resource searching --- Searching, Bibliographical --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Management. --- Automation --- -Management --- Reference department --- -Management. --- Libraries --- Management --- Automation&delete&

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such

Text processing (Computer science) --- Sorting (Electronic computers) --- Relevance --- Database searching --- Search engines --- Engineering & Applied Sciences --- Computer Science --- Programming --- Relevance. --- Database searching. --- Programming. --- Computer programming --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching --- Pertinence --- Relevancy --- Meaning (Philosophy) --- Meaning (Psychology) --- Computer sorting --- Electronic data processing --- Processing, Text (Computer science) --- Database management --- Information storage and retrieval systems --- Word processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods. Performance improvement techniques that work by transforming the input or output. Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.

Data mining. --- Database searching. --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching