Scientific Data Analysis using Jython Scripting and Java

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Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included. Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.

Author(s): Sergei V. Chekanov (auth.)
Series: Advanced Information and Knowledge Processing
Edition: 1
Publisher: Springer-Verlag London
Year: 2010

Language: English
Pages: 440
Tags: Computer Science, general; Data Mining and Knowledge Discovery

Front Matter....Pages I-XXIV
Introduction....Pages 1-2
Jython, Java and jHepWork....Pages 3-26
Introduction to Jython....Pages 27-84
Mathematical Functions....Pages 85-120
One-dimensional Data....Pages 121-134
Two-dimensional Data....Pages 135-159
Multi-dimensional Data....Pages 161-169
Arrays, Matrices and Linear Algebra....Pages 171-192
Histograms....Pages 193-221
Random Numbers and Statistical Samples....Pages 223-233
Graphical Canvases....Pages 235-271
Input and Output....Pages 273-312
Miscellaneous Analysis Issues Using jHepWork....Pages 313-334
Data Clustering....Pages 335-342
Linear Regression and Curve Fitting....Pages 343-365
Neural Networks....Pages 367-382
Steps in Data Analysis....Pages 383-405
Real-life Examples....Pages 407-433
Back Matter....Pages 435-440