Data mining techniques : for marketing, sales, and customer relationship management / Gordon S Linoff, Michael J Berry.
Material type: TextPublication details: Indianapolis, IN : Wiley Pub., 2011.Edition: 3rd edDescription: xl, 847 p. : ill. ; 24 cmISBN:- 9780470650936 (pbk : alk. paper)
- 0470650931 (pbk : alk. paper)
- 9781118087459 (ebk)
- 9781118087473 (ebk.)
- 9781118087503 (ebk.)
- HF5415.125 .B47 2011
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|
Books | Zetech Library - TRC General Stacks | Non-fiction | HF5415.125 .B47 2011 (Browse shelf(Opens below)) | C1 | Not For Loan | Z003513 |
Browsing Zetech Library - TRC shelves, Shelving location: General Stacks, Collection: Non-fiction Close shelf browser (Hides shelf browser)
HF5415.12.A89 .D48 2000 Developments in Australasian Marketing / | HF5415.122 .L68 1998 Services Marketing : | HF5415.123.S54 1997 Advertising, promotion, and supplemental aspects of integrated marketing communications / | HF5415.125 .B47 2011 Data mining techniques : | HF5415.125 .B53 2019 Data science for marketing analytics: achieve your marketing goals with the data analytics power of python / | HF5415.1265 .D66 1991 Direct line to profits : | HF5415.1265 .S39 2009 The new rules of marketing and PR : |
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.
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