DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Data Mining Introduction Getting Started Data Loading Data Exploring Data Interactive Graphics Test Descriptive Data Mining Predictive Data Mining Evaluation and Deployment Data Cleaning Handling Missing Data Transforming Data Data Reduction Deployment Troubleshooting Issues Moving into R Beyond Rattle R Getting Help Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Topics in Data Mining Fraud Analysis Archetype Analysis Text Mining Survival Analysis Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models 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 Appendices Installing Rattle Bibliography Index Preface Goals Organisation Features Audience Typographical Conventions A Note on Languages Currency Acknowledgements

Descriptive and Predictive Analytics

In this chapter we introduce the concepts of descriptive and predictive Analytics

The following chapters are then devoted to particular algorithms.

Modelling is what people often think of when they think of data mining. Modelling is the process of taking some data (usually) and building a model that reflects that data. Usually the aim is to address a specific problem through modelling the world in some way and from the model develop a better understanding of the world.

There is a bewildering array of tools and techniques at the disposal of the data miner. We can get a better understanding of what is available through categorising the algorithms according to the types of analysis performed. In this chapter we introduce and summarise the broader categories of data mining analysis. Part IV then presents, in a systematic manner, many algorithms that are used in data mining and available either freely or else implemented in commercial toolkits.

Much of the terminology used in data mining has grown out of that used in both machine learning and statistics. We identify, for example, two very broad categories of analysis as unsupervised and supervised (as in supervised and unsupervised learning).

We introduce such an ordering to the world of data mining techniques in this chapter. In summary:

Support further development through the purchase of the PDF version of the book.
The PDF version is a formatted comprehensive draft book (with over 800 pages).
Brought to you by Togaware. This page generated: Saturday, 16 January 2010