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

Data Cleaning

Data cleaning deals with issues of removing errant transactions, updating transactions to account for reversals, elimination of missing data, and so on.

The aim of data cleaning is to raise the data quality to a level suitable for the selected analyses.

The data cleaning to be performed depends on purpose to which the data is to be put. Some activities will require a selection of data cleaning and data transformation modules to be applied to the data.

Data cleaning occurs early in the process and then continually throughout the process as we learn more about the data.

Field selection

Sampling

Data correction

Missing values treatment

Data transformation, e.g., birth date to age.

Derive new fields

Useful steps:

Understand the business problem.

Collect the materials about the data sources and study them to understand what data is available.

Identify the data items relevant to the business problem, e.g., tables and attributes.

Make a data extraction plan and arrange the data extraction (with DBAs).

Calculate the summary statistics of the extracted data.

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