DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining 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 Glossary Bibliography Index

Measuring Data Distributions

We now start to explore how the data in each of the variables is distributed. This might be as simple as looking at the spread of the numeric values, or the number of entities having a specific value for a variable. Another aspect involves measuring the central tendency of data, or determining the http://en.wikipedia.org/wiki/meanmean and http://en.wikipedia.org/wiki/medianmedian. Yet another is a measure of the spread or http://en.wikipedia.org/wiki/variancevariance of the data from this central tendency. We again begin with textual presentations of the distributions, and then graphical presentations.

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