Data mining techniques :
Linoff, Gordon S.
Data mining techniques : for marketing, sales, and customer relationship management / Gordon S Linoff, Michael J Berry. - 3rd ed. - Indianapolis, IN : Wiley Pub., 2011. - xl, 847 p. : ill. ; 24 cm.
Berry's name appears first on the 2nd ed.
Includes index.
What is data mining and why do it? -- Data mining applications in marketing the customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: Profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making data mean more -- Too much of a good thing? Techniques for reducing the number of variables -- Listen carefully to what your customers say: Text mining.
9780470650936 (pbk : alk. paper) 0470650931 (pbk : alk. paper) 9781118087459 (ebk) 9781118087473 (ebk.) 9781118087503 (ebk.)
2011921769
Data mining.
Marketing--Data processing.
Business--Data processing.
HF5415.125 / .B47 2011
Data mining techniques : for marketing, sales, and customer relationship management / Gordon S Linoff, Michael J Berry. - 3rd ed. - Indianapolis, IN : Wiley Pub., 2011. - xl, 847 p. : ill. ; 24 cm.
Berry's name appears first on the 2nd ed.
Includes index.
What is data mining and why do it? -- Data mining applications in marketing the customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: Profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making data mean more -- Too much of a good thing? Techniques for reducing the number of variables -- Listen carefully to what your customers say: Text mining.
9780470650936 (pbk : alk. paper) 0470650931 (pbk : alk. paper) 9781118087459 (ebk) 9781118087473 (ebk.) 9781118087503 (ebk.)
2011921769
Data mining.
Marketing--Data processing.
Business--Data processing.
HF5415.125 / .B47 2011