DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle Glossary Bibliography Index

Temporal Analysis

A common data mining task with temporal data is to find repeating patterns in the data - see frequent closed itemsets.

SNN Clustering Lin et al. (2000) and Lin et al. (2001).

A common question is how likely an event will occur at a give point in time. Suppose we had some simple churn data recording how long a customer has been with a telecoms provider before they churned.

ID   Gender  Months     Churn
1       M        12         1
2       M         5         0
3       M        32         1
4       M         4         0
5       M        10         1
6       F        12         0
7       F         5         1
8       F        15         0
9       F         5         1
10      F        12         0

We may be tempted in the first instance to us a logistic regression including XnullXRattle!VariablesR functions (R function)Rattle!VariablesR libraries (R library)Rattle!VariablesR option (R option)Rattle!VariablesR packages (R package)Rattle!VariablesDatasets (Dataset)Rattle!VariablesRattle!VariablesGender and XnullXRattle!VariablesR functions (R function)Rattle!VariablesR libraries (R library)Rattle!VariablesR option (R option)Rattle!VariablesR packages (R package)Rattle!VariablesDatasets (Dataset)Rattle!VariablesRattle!VariablesMonths to predict XnullXRattle!VariablesR functions (R function)Rattle!VariablesR libraries (R library)Rattle!VariablesR option (R option)Rattle!VariablesR packages (R package)Rattle!VariablesDatasets (Dataset)Rattle!VariablesRattle!VariablesChurn with:

$$logit(Churn=1)=b_0+b_1*Gender+b_2*Tenure$$

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