1,720,986 research outputs found
Data Mining in Medicine
No full textClinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems
Discovering new rule induction algorithms with grammar-based genetic programming
No full textRule induction is a data mining technique used to extract classification rules of the form IF (conditions) THEN (predicted class) from data. The majority of the rule induction algorithms found in the literature follow the sequential covering strategy, which essentially induces one rule at a time until (almost) all the training data is covered by the induced rule set. This strategy describes a basic algorithm composed by several key elements, which can be modified and/or extended to generate new and better rule induction algorithms. With this in mind, this work proposes the use of a grammar-based genetic programming (GGP) algorithm to automatically discover new sequential covering algorithms. The proposed system is evaluated using 20 data sets, and the automatically-discovered rule induction algorithms are compared with four well-known human-designed rule induction algorithms. Results showed that the GGP system is a promising approach to effectively discover new sequential covering algorithms
Evolutionary Algorithms for Data Mining
No full textEvolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of Darwinian evolution. The motivation for applying EAs to Data Mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter reviews mainly two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP), and discusses how EAs can be applied to several Data Mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. It also discusses the basic idea of Multi-Objective EAs, based on the concept of Pareto dominance, which also has applications in Data Mining
Clustering High-Dimensional Data
No full textClustering algorithms have been adapted or specifically designed for high-dimensional data where many attributes might be just noise such that patterns can be identified only in appropriate combinations of attributes and would be obfuscated by noise otherwise. In this chapter, we give an overview of the basic strategies and techniques used for these specialized algorithms along with pointers to example methods
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