DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Rattle Introduction Getting Started Interacting with Rattle Data Exploring Data Test Transforming Data Cluster Analysis Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting 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 Installing Rattle Glossary Bibliography Index

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Data Cube: A block of data, often extracted from a Refer[data-warehouse]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 associations, Referclassification, and Refersegmentation, on problem domains that require analysing extremely large collections of data, including domains such as fraud and Referchurn. Typically draws on tools and techniques from machine learning, Parallel Computation, OLAP, visualisation, Mathematical Computation, and Referstatistics. Fayyad defines data mining as a single step in the Refer[kdd]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 Refer[data-mining]Data Mining in Refer[data-cube]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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