國立虎尾科技大學 |

Pattern Mining with Evolutionary Algorithms

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Pattern Mining with Evolutionary Algorithms/ by Sebastián Ventura, José María Luna.
Author:	Ventura, Sebastián.
other author:	Luna, José María.
Description:	XIII, 190 p. 126 illus., 4 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Pattern recognition. -
Online resource:	https://doi.org/10.1007/978-3-319-33858-3
ISBN:	9783319338583

Pattern Mining with Evolutionary Algorithms
Ventura, Sebastián.

Pattern Mining with Evolutionary Algorithms[electronic resource] /by Sebastián Ventura, José María Luna. - 1st ed. 2016. - XIII, 190 p. 126 illus., 4 illus. in color.online resource.

Introduction to Pattern Mining -- Quality Measures in Pattern Mining -- Introduction to Evolutionary Computation -- Pattern Mining with Genetic Algorithms -- Genetic Programming in Pattern Mining -- Multiobjective Approaches in Pattern Mining -- Supervised Local Pattern Mining -- Mining Exceptional Relationships Between Patterns -- Scalability in Pattern Mining. .

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness. .

ISBN: 9783319338583

Standard No.: 10.1007/978-3-319-33858-3doiSubjects--Topical Terms:

1253525
Pattern recognition.

LC Class. No.: Q337.5

Dewey Class. No.: 006.4

Pattern Mining with Evolutionary Algorithms
LDR:03775nam a22004095i 4500 001 978370
003 DE-He213
005 20200705030806.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319338583 $9 978-3-319-33858-3
024 7 $a 10.1007/978-3-319-33858-3 $2 doi
035 $a 978-3-319-33858-3
050 4 $a Q337.5
050 4 $a TK7882.P3
072 7 $a UYQP $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQP $2 thema
082 0 4 $a 006.4 $2 23
100 1 $a Ventura, Sebastián. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1271855
245 1 0 $a Pattern Mining with Evolutionary Algorithms $h [electronic resource] / $c by Sebastián Ventura, José María Luna.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XIII, 190 p. 126 illus., 4 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction to Pattern Mining -- Quality Measures in Pattern Mining -- Introduction to Evolutionary Computation -- Pattern Mining with Genetic Algorithms -- Genetic Programming in Pattern Mining -- Multiobjective Approaches in Pattern Mining -- Supervised Local Pattern Mining -- Mining Exceptional Relationships Between Patterns -- Scalability in Pattern Mining. .
520 $a This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness. .
650 0 $a Pattern recognition. $3 1253525
650 0 $a Data mining. $3 528622
650 0 $a Algorithms. $3 527865
650 1 4 $a Pattern Recognition. $3 669796
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 593923
700 1 $a Luna, José María. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1271856
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319338576
776 0 8 $i Printed edition: $z 9783319338590
776 0 8 $i Printed edition: $z 9783319816180
856 4 0 $u https://doi.org/10.1007/978-3-319-33858-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)