CEE 5244 Lecture Notes - Lecture 41: Palomar Observatory, Data Mining

• School: Temple University
• Department: Civil Engineering
• Course: CEE 5244
• Professor: Dr.Arch

Document Summary

Lecture - 41: data mining tasks, classification [predictive, clustering [descriptive, association rule discovery [descriptive, sequential pattern discovery [descriptive, regression [predictive, deviation detection [predictive, classification: definition, given a collection of records (training set ) A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it: classification example, classification: application 1, direct marketing. Goal: reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. Approach: (cid:1) use the data for a similar product introduced before. (cid:1) we know which customers decided to buy and which decided otherwise. This {buy, don"t buy} decision forms the class attribute. (cid:1) collect various demographic, lifestyle, and company- interaction related information about all such customers.