Chapter 7 – Basic Algorithms for Data Mining: A Brief Overview
Robert Nisbet · 2009
Publisher Summary This chapter discusses the basic algorithms used in data mining and helps to select the right one to use. It presents two semi-automated approaches to performing all the necessary operations from accessing data to producing model results. The first example shows how STATISTICA Data Miner Recipe (DMR recipe) Interface packages all basic steps of a data mining project into an easy-to-use interface. The second example is KXEN (Knowledge Extraction Engine). Both tools select the modeling algorithms and permit to enter a few settings, and automatically generate model results. Use of either tool might be the best way for beginning data miners to build their first model. The DMRecipe Interface provides a step-by-step approach to data preparation, variable selection, and dimensionality reduction, resulting in models trained with different algorithms. The automated functions of DMRecipe and KXEN Modeling Assistant provide a glimpse of one direction in which data mining is developing. These tools provide a close analogy in which data mining is as easy to use as the automobile interface. Association algorithms can be used to analyze simple categorical variables, dichotomous variables, and/or multiple target variables. The goal of association rules is to detect relationships or associations between specific values of categorical variables in large data sets. This technique allows analysts and researchers to uncover hidden patterns in large data sets.