|
Title:
|
INFORMATION RETRIEVAL WITH CLUSTER GENETIC |
|
Author(s):
|
José Luis Castillo Sequera , José R. Fernández Del Castillo , León González Sotos |
|
ISBN:
|
978-972-8924-63-8 |
|
Editors:
|
Hans Weghorn and Ajith P. Abraham |
|
Year:
|
2008 |
|
Edition:
|
Single |
|
Keywords:
|
Clustering, Information Retrieval, Optimization methods, Data mining |
|
Type:
|
Short Paper |
|
First Page:
|
77 |
|
Last Page:
|
81 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
This article presents an online cluster using genetic algorithms to increase information retrieval efficiency. The
Information Retrieval (IR) is based on the grouping of documents. Documents with high similarity to group are judge
more relevant to the query and should be retrieved more efficiently. Under genetic algorithms, an individual is a
hierarchical chromosome with all the documents of a documental base; and we generate a population of different
individuals. These chromosomes feed into genetic operator process: selection, crossover, and mutation until we get
an optimize cluster chromosome for document retrieval. Our testing result show that information retrieval with 0.9
crossover probability and 0.65 mutation probability give the highest precision while lower crossover probability and high
mutation probability give the highest recall. |
|
|
|
|
|
|
