33_091111_CE16_DataMining

33_091111_CE16_DataMining - MIS 301 Introduction to IT...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: MIS 301 Introduction to IT Management Lizhen Xu IROM Department McCombs School of Business Data Mining Ch Ex 16 Database Marketing Class Business • Excel #3 Assignment – Under BB->Assignments – XL Miner, available in Millennium Lab – Alone or with a partner – Deadline Extended : Monday, Nov 16, beginning of class, Hard Copy Recap • PB? EB? • 4 types of BI systems? – Reporting system; – Data mining system; – Knowledge management system; – Expert system; • Problems of operational data – Wrong granularity : Too fine? Too coarse? • Operational Database E Data Warehouse E Data Mart What is Data Mining? • Definition : Non-trivial discovery of novel, valid, comprehensible and potentially useful patterns from data • Pattern: relationship in data – E.g., People with bad credit scores are more likely to have accidents. – On Thursday nights, people who buy diapers also tend to buy beer • Two categories: • Unsupervised • Supervised Unsupervised Data Mining • Analysts do not create model before running analysis • Apply data-mining technique and observe results • Hypotheses created after analysis as explanation for results • Example: cluster analysis Supervised Data Mining • Model developed before analysis • Statistical techniques used to estimate parameters • Examples: – Regression analysis – Neural networks 1 2 3 4 1 class grade age major β β β β = + ⋅ + ⋅ + ⋅ Data Mining Tasks • Data mining commonly involves four classes of task : – Clustering – Classifications – Regressions – Association detection Clustering • Cluster Analysis – Similar records (or characteristics) are group together – Does not rely on predefined categories ; being grouped together on...
View Full Document

{[ snackBarMessage ]}

Page1 / 29

33_091111_CE16_DataMining - MIS 301 Introduction to IT...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online