DMCA
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction. (0)
| Venue: | Stanford University |
| Citations: | 47 - 5 self |
BibTeX
@INPROCEEDINGS{Freitas_agenetic,
author = {Alex Freitas},
title = {A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction.},
booktitle = {Stanford University},
year = {},
pages = {96--101},
publisher = {Morgan Kaufmann}
}
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Abstract
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization. The paper also proposes some genetic operators tailored for the two above data mining tasks. 1. Introduction Data Mining (DM) consists of the extraction of interesting, novel knowledge from real-world databases [Fayyad et al. 96]. DM is an interdisciplinary subject, whose core lies at the intersection of machine learning and databases. Four desirable characteristics of a DM system are: (1) the discovery of comprehensible knowledge, typically expressed by high-level rules; (2) integration with databases [Ha...
Keyphrases
data mining task generalized rule induction genetic programming framework interdisciplinary subject major data mining task data-privacy control desirable characteristic sql query automatic parallelization dm system novel knowledge comprehensible knowledge genetic programming real-world database database server genetic operator high-level rule relational database system gp algorithm data mining viewpoint machine learning introduction data mining
