Advanced Data Mining Techniques - download pdf or read online

By David L. Olson Dr., Dursun Delen Dr. (auth.)

ISBN-10: 3540769161

ISBN-13: 9783540769163

ISBN-10: 354076917X

ISBN-13: 9783540769170

This ebook covers the elemental ideas of knowledge mining, to illustrate the opportunity of amassing huge units of knowledge, and studying those facts units to realize priceless enterprise figuring out. The e-book is prepared in 3 elements. half I introduces strategies. half II describes and demonstrates simple facts mining algorithms. It additionally comprises chapters on a few diverse recommendations usually utilized in information mining. half III focusses on enterprise purposes of information mining. equipment are offered with basic examples, functions are reviewed, and relativ merits are evaluated.

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Extra resources for Advanced Data Mining Techniques

Example text

Part II DATA MINING METHODS AS TOOLS 3 Memory-Based Reasoning Methods Memory-based reasoning systems are a type of model, supporting the modeling phase of the data mining process. Their unique feature is that they are relatively machine driven, involving automatic classification of cases. It is a highly useful technique that can be applied to text data as well as traditional numeric data domains. 1 It operates by comparing new unclassified records with known examples and patterns. The case that most closely matches the new record is identified, using one of a number of different possible measures.

An association rule mining-based methodology for automated detection of ischemic ECG beats, IEEE Transactions on Biomedical Engineering 53:8, 1531–1540. -C. Tsoi, S. Zhang, M. Hagenbuchner (2005). Pattern discovery on Australian medical claims data – A systematic approach, IEEE Transactions on Knowledge & Data Engineering 17:10, 1420–1435. J. Buddhakulsomsiri, Y. Siradeghyan, A. Zakarian, X. Li (2006). Association rule-generation algorithm for mining automotive warranty data, International Journal of Production Research 44:14, 2749–2770.

For each account, variables were defined by counting the appropriate measure for every 2-week period in the critical period for that observation. At the end of this phase, new variables were created to describe phone usage by account compared to a moving average of four previous 2-week periods. At this stage, there were 46 variables as candidate discriminating factors. These variables included 40 variables measured as call habits over Handling Data 31 15 two-week periods, as well as variables concerning the type of customer, whether or not a customer was new, and four variables relating to customer bill payment.

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Advanced Data Mining Techniques by David L. Olson Dr., Dursun Delen Dr. (auth.)


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