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

D

Data Cube: A block of data, often extracted from a data warehouse, offering fast access/views of data in any number of dimensions.

Data Mining: A technology concerned with using a variety of techniques, including http://en.wikipedia.org/wiki/Association_(statistics)associations, classification, and segmentation, on problem domains that require analysing extremely large collections of data, including domains such as fraud and churn. Typically draws on tools and techniques from machine learning, Parallel Computation, http://en.wikipedia.org/wiki/olapOLAP, visualisation, Mathematical Computation, and statistics. Fayyad defines data mining as a single step in the KDD process that under acceptable computational efficiency limitations, enumerates structures (patterns or models) over the data.

Data Warehouse: Provides a multi-dimensional view of an organisations data for efficient analysis as distinct from typical transactional relational database systems which manage the day-to-day operations of the organisation. Data warehouses particularly lend themselves to quick ad-hoc queries on large volumes of data on a read-only basis. Inmon defined a data warehouse as a subject-oriented, integrated, time variant and non-volatile collection of data in support of management's decision-making process.

Decision Tree:

Demographic Clustering: Clustering performed on the characteristics of population groups in terms of size, distribution, and other vital statistics, rather than on the individuals of the population. Related to Data Mining in Data Cubes in that Data Cubes allow varying degrees of aggregation over any of the variables.

Dependent Variable: The classical term for an Output Variable.

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