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Rescooped by Ramnandan Krishnamurthy from RTREE
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Foundations of Multidimensional and Metric Data Structures - Free eBook Share

Foundations of Multidimensional and Metric Data Structures - Free eBook Share | ml | Scoop.it
eBook Free Download: Foundations of Multidimensional and Metric Data Structures | PDF, EPUB | ISBN: 0123694469 | 2006-08-22 | English | PutLocker

Via Fox eBook, Bill Soumakis
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Fox eBook's curator insight, August 1, 2013 11:32 PM

Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in ComputerGraphics)

The field of multidimensional data structures is large and growing very quickly. Here, for the first time, is a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Along with the hundreds of worked exercises and hundreds of illustrations, the result is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programmingimage processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design. Award Winner in 2006 “Best Book” competition in Professional and Scholarly Publishing from the Association of American Publishers.

Morgan Kaufmann would like to congratulate Hanan Samet on receiving the UCGIS 2009 Research Award!

Read the announcement here: http://www.ucgis.org/summer2009/researchaward.htm

* First comprehensive work on multidimensional data structures available, a thorough and authoritative treatment.
* An algorithmic rather than mathematical approach, with a liberal use of examples that allows the readers to easily see the possible implementation and use.
* Each section includes a large number of exercises and solutions to self-test and confirm the reader’s understanding and suggest future directions.
* Written by a well-known authority in the area of spatial data structures who has made many significant contributions to the field.

The author’s website includes: Spatial Index Demos

Table of Contents

Chapter 1. Multidimensional Point Data
Chapter 2. Object-Based and Image-Based Image Representations
Chapter 3. Intervals and Small Rectangles
Chapter 4. High-Dimensional Data
Appendix A: Overview of B-Trees
Appendix B: Linear Hashing
Appendix C: Spiral Hashing
Appendix D: Description of Pseudocode Language
Solutions to Exercises

Scaraffe Rao's curator insight, August 26, 2013 7:35 AM

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Rescooped by Ramnandan Krishnamurthy from Big Data Technologies
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Feature Selection with Scikit-Learn

Feature Selection with Scikit-Learn | ml | Scoop.it

In the code below, I compute the accuracies with various feature sizes for 9 different classifiers, using both the Chi-squared measure and the ANOVA F measures.


Via Dahl Winters
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