%0 Book %1 Bramer16 %A Bramer, Max %B Undergraduate Topics in Computer Science %C London %D 2013 %I Springer %K 01624 103 springer book ai data pattern recognition information analysis intro %R 10.1007/978-1-4471-7307-6 %T Principles of Data Mining %X Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. %7 3 %@ 978-1-4471-7306-9

@book{Bramer16, abstract = {Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.}, added-at = {2017-04-05T15:43:40.000+0200}, address = {London}, author = {Bramer, Max}, biburl = {https://www.bibsonomy.org/bibtex/252d9ba358f49174819342de3f7ecb21e/flint63}, doi = {10.1007/978-1-4471-7307-6}, edition = 3, file = {SpringerLink:2016/Bramer16.pdf:PDF;Springer Product page:http\://www.springer.com/978-1-4471-7306-9:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1447173066/:URL}, groups = {public}, interhash = {512df159fe7a3230bde7f07d8ba77421}, intrahash = {52d9ba358f49174819342de3f7ecb21e}, isbn = {978-1-4471-7306-9}, issn = {1863-7310}, keywords = {01624 103 springer book ai data pattern recognition information analysis intro}, publisher = {Springer}, series = {Undergraduate Topics in Computer Science}, timestamp = {2017-07-13T17:29:24.000+0200}, title = {Principles of Data Mining}, username = {flint63}, year = 2013 }