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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Lazy Learning: While traditional learning algorithms compile data into abstractions in the process of inducing concept descriptions, lazy learning algorithms, also known as instance-based, memory-based, exemplar-based, experience-based, and case-based, learning delay this process and represent concept descriptions with the data itself. Lazy learning has its roots in disciplines such as pattern recognition, cognitive psychology, statistics, cognitive science, robotics, and information retrieval. It has received increasing attention in several AI disciplines as researchers have explored issues on massively parallel approaches, cost sensitivity, matching algorithms for use with symbolic and structured data representations, formal analyses, rule extraction, variable selection, interaction with knowledge-based systems, integration with other learning/reasoning approaches, and numerous application-specific issues. Many reasons exist for this level of activity: these algorithms are relatively easy to present and analyse, are easily applied, have promising performance on some measures, and are the basis for today's commercially popular case-based reasoning systems.

Learning Curve: A plot of accuracy against training set size, often used to determine the smallest training set size for which adding extra instances leads to little, if any, improvement of accuracy.

Leverage: A measure of Referinterestingness in terms of the difference between the observed frequency of occurrence of items and the expected frequency if the items were independent.

Lift: A measure of Referinterestingness capturing the increase in the likelihood of an item occurring, for example, within a defined sub population, compared to the full population.

Link Analysis: Explores associations among entities. For example, a law enforcement application might examine familial relationships among suspects and victims, the addresses at which those persons reside, and the telephone numbers that they called during a specified period. The ability of link analysis to represent relationships and associations among entities of different types has proven crucial in assisting human investigators to comprehend complex webs of evidence and draw conclusions that are not apparent from any single piece of information. Computer-based link analysis is increasingly used in law enforcement investigations, fraud detection, telecommunications network analysis, pharmaceuticals research, epidemiology, and many other specialised applications. Also referred to as Social Network Analysis. David Jensen jensen@cs.umass.edu, 30 Jan 98, datamine-l@nautilus-sys.com

Logistic Regression:

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