Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little “how to” information? And are you, like most analysts, preparing the data in SAS?
This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.
I EKSPLORACJA DANYCH Eksploracja danych: literatura Pyle D., Data preparation for Data Mining, Morgan Kaufmann Publishers, Academic Press, 1999 Larose D.T., Odkrywanie wiedzy z danych, Wydawnictwo Naukowe PWN, Warszawa 2006, Hand, D., Mannila H., Smyth P., Eksploracja danych, Wydawnictwo NT, Warszawa 2005 Witten I.H., Frank E., Data Mining. Practical Machine Learning Tools and Technics, Elsevier 2005 Han J., Kamber M., Data Mining. Concepts and Techniques, Morgan Kaufmann Publishers 2001 Hand, D., Mannila H., Smyth P., Principles of Data Mining, The MIT Press 2001
Data mining lets organizations discover patterns and trends in their data to make better decisions, but where analysts struggle is getting the data ready for analysis within the data mining cycle. According to research, data preparation for data mining can consume 60-80% of analysts time, and less time refining models and analysis. Alteryx allows analysts to flip this scenario by cutting the data preparation time up to 30%, giving analysts more time to test hypothesis and evaluate models. Alteryx does this through self-service data analysis that lets analysts:
It is easy to write books that address broad topics and ideas leaving the reader with the question "Yes, but how? By combining a comprehensive guide to data preparation for data mining along with
Data Preparation for Data Mining, Volume 1
No preview available - 1999