DATA MINING: A TUTORIAL-BASED PRIMER

By:

ROIGER RICHARD

Material type: Text

TextPublication details: UK CRC PRESS 2017Edition: 2nd edDescription: xxxviii, 487ISBN:

978-1-4987-6397-4

Subject(s):

DATA MINING

DDC classification:

006.312/ROI

Summary: This book provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

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Holdings
Item type	Current library	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Data Mining	006.312/ROI (Browse shelf(Opens below))	Available	DATA MINING		SICSR-B-19524

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Previous	006.3/SAN Deep Reinforcement Learning with Python	006.3/VER Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras	006.312/BAR LEARNING TO LOVE DATA SCIENCE	006.312/ROI DATA MINING: A TUTORIAL-BASED PRIMER	006.35/KUL/SHI Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques	5.471 HAN/KAM DATA MINING:CONCEPTS AND TECHNIQUES	5.741 CHA MINING THE WEB:DISCOVERING KNOWLEDGE FROM HYPERTEXT DATA

This book provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools.
Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more.
The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

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