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

L

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 interestingness 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 interestingness 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:

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.