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Database Modeling and Design: Logical Design, 5th Edition. Toby J. Teorey, Sam S. Lightstone, Thomas P. Nadeau, H. V. Jagadish. Foundations of Multidimensional and Metric Data Structures. Joe Celko’s SQL for Smarties: Advanced SQL Programming, 4th Edition. Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration.
data mining for business analytics concepts, techniques and applications in python galit shmueli | peter c. bruce peter gedeck | nitin r. patel
Data are typically stored in a database environment and are large in scale. Data mining is also referred to as knowledge discovery in database (KDD). Business intelligence is the transformation of raw data into knowledge and insight for making better business decisions.
This book provides an introduction to key concepts in data mining and business intelligence. It covers topics such as data exploration, preprocessing, classification, clustering, outlier analysis, frequent pattern mining, and applications of data mining and business intelligence.
18 Απρ 2016 · Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and ...
1 Ιαν 2016 · The objective of this paper is to present a review literature on what are impacts of Data Mining (DM) in Business Intelligence (BI). The paper highlights various features of DM. It involves...
18 Νοε 2005 · This paper presents a meta-modelling architecture for data mining that automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing data mining techniques.
