Unformatted text preview: SPSS Clementine for Data Mining in Institutional Research C. R. Thulasi Kumar Office of Information Management & Analysis University of Northern Iowa November 10-12, 2004 Overview
What Data Mining IS and IS NOT? Various Data Mining Techniques Steps in the Data Mining Process CRISP-DM Examples of Data Mining Applications Data Mining Issues Questions What is Data Mining?
The exploration and analysis of large quantities of data in order to discover meaningful patterns and rules (Berry and Linoff). A user-centric, interactive process which leverages analysis technologies and computing power. A group of techniques that find relationships that have not previously been discovered. A relatively easy task that requires knowledge of the business problem/subject matter expertise. "Computers and algorithms don't mine data; people do!"
Source: Laura Squier, SPSS BI Data Mining Applications in Institutional Research
Student academic success/Retention and graduation Identify high risk students Predict course demand and pattern Profile good transfer candidates Application success rates Predict potential alumni donations SPSS Data Mining Techniques
Technique 1. Predictive Method 1. Neural Networks 2. Rule Induction 3. Linear & Logistic Regression 4. Sequence Detection 1. Kohonen Networks 2. K-Means Clustering 3. Two-Step Clustering 1. APRIORI 2. GRI 3. CARMA Types C5.0 and C & R Tree 2. Clustering 3. Association Rules Selecting the Appropriate Modeling Technique
Categorize your students Classification
Predict students success Rule Induction Classification and Regression Trees Neural Networks Regression Kohonen Networks K-Means Clustering Two-Step Clustering APRIORI GRI CARMA Capri Rule Induction Prediction
Group similar students Segmentation
Identify courses that are taken together Association
Find patterns and trends over time Sequence Phases in the DM Process: CRISP-DM Source: SPSS BI Phases and Tasks
Business Understanding Data Understanding...
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- Fall '09
- Data Mining