Data Fusion in Information Retrieval [electronic resource] / by Shengli Wu.

By: Wu, Shengli [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Adaptation, Learning, and Optimization: 13Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012Description: XII, 228 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642288661Subject(s): Engineering | Data mining | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Introduction -- Evaluation of Retrieval Results -- Score Normalization -- Observations and Analyses -- The Linear Combination Method -- A Geometric Framework for Data Fusion -- Ranking-Based Fusion -- Fusing Results from Overlapping Databases -- Application of the Data Fusion Technique.
In: Springer eBooksSummary: The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: -          What are the key factors that affect the performance of data fusion algorithms significantly? -          What conditions are favorable to data fusion algorithms? -          CombSum and CombMNZ, which one is better? and why? -          What is the rationale of using the linear combination method? -          How can the best fusion option be found under any given circumstances?
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Introduction -- Evaluation of Retrieval Results -- Score Normalization -- Observations and Analyses -- The Linear Combination Method -- A Geometric Framework for Data Fusion -- Ranking-Based Fusion -- Fusing Results from Overlapping Databases -- Application of the Data Fusion Technique.

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: -          What are the key factors that affect the performance of data fusion algorithms significantly? -          What conditions are favorable to data fusion algorithms? -          CombSum and CombMNZ, which one is better? and why? -          What is the rationale of using the linear combination method? -          How can the best fusion option be found under any given circumstances?

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