DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Rattle Data Miner Sourcing Data Selecting Data Exploring Data Transforming Data Building Classification Models Unsupervised Modelling Model Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models 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 Glossary Bibliography Index

Normalise

Different model builders require different characteristics of the data they are building their models from. For example, in build a clustering using any kind of distance measure, we may need to normalise the data. Otherwise, a variable like Income will overwhelm a variable like Age, when calculating distances. A distance of 10 ``years'' may be more significant than a distance of $10,000, yet, $10000$ swamps $10$ when they are added together, as would be the case by calculating distances.

The Normalise option of the Transform tab can perform a number of normalisations, including re-centering and rescaling our data to be around zero (Recenter), rescaling our data to be in the range from 0 to 1 (Scale [0,1]), covert the numbers into a rank ordering (Rank), and finally, to do a robust rescaling around zero using the median (-Median/MAD). Figure 6.2 displays the interface.

Figure 6.2: Selection of normalisations.

We can see in Figure 6.2 the apprach we take to normalising (and to transforming) our data. The original data is not modified. Instead, a new variable is created with a prefix added to the variable's name that indicates the kind of transformation. As we can see in the figure, the prefixes are NORM_RECENTER_, NORM_SCALE01_, NORM_RANK_, and NORM_MEDIANAD_.

We can see the effect of the four normalisations in comparing the histogram of the variable, Age, in Figure 6.3, with the four plots in Figure 6.4 for the corresponding four normalisations.

Figure 6.3: Normalisations of Age.

Figure 6.4: Normalisations of Age.

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