Supervised Descriptive Pattern Mining. (2018)
- Record Type:
- Book
- Title:
- Supervised Descriptive Pattern Mining. (2018)
- Main Title:
- Supervised Descriptive Pattern Mining
- Further Information:
- Note: Sebastian Ventura, Jose Maria Luna.
- Authors:
- Ventura, S (Sebastian)
Luna, José María - Contents:
- Intro; Acknowledgments; Contents; 1 Introduction to Supervised Descriptive Pattern Mining; 1.1 Patterns in Data Analysis; 1.2 Pattern Mining: Types of Patterns and Advanced Data Types; 1.2.1 Frequent/Infrequent Patterns; 1.2.2 Positive/Negative Patterns; 1.2.3 Maximal/Closed/Colossal Patterns; 1.2.4 Condensed Patterns; 1.2.5 Patterns on Advanced Data Types; 1.2.5.1 Data Streams; 1.2.5.2 Sequential Data; 1.2.5.3 Spatiotemporal Data; 1.2.5.4 Graphs; 1.2.5.5 High Utility Data; 1.2.5.6 Uncertain Data; 1.2.5.7 Multiple-Instance Data; 1.3 Supervised Descriptive Patterns; 1.3.1 Contrast Sets 1.3.2 Emerging Patterns1.3.3 Subgroup Discovery; 1.3.4 Class Association Rules; 1.3.5 Exceptional Models; 1.3.6 Other Forms of Supervised Descriptive Patterns; 1.4 Scalability Issues; 1.4.1 Heuristic Approaches; 1.4.2 New Data Structures; 1.4.3 Parallel Computing; 1.4.4 MapReduce Framework; References; 2 Contrast Sets; 2.1 Introduction; 2.2 Task Definition; 2.2.1 Quality Measures; 2.2.2 Tree Structures; 2.2.2.1 Frequent Tree Ordering; 2.2.2.2 Difference Ordering; 2.2.2.3 Hybrid Ordering; 2.3 Algorithms for Mining Contrast Sets; 2.3.1 STUCCO; 2.3.2 CIGAR; 2.3.3 CSM-SD 2.3.4 Additional ApproachesReferences; 3 Emerging Patterns; 3.1 Introduction; 3.2 Task Definition; 3.2.1 Problem Decomposition; 3.2.2 Types of Emerging Patterns; 3.3 Algorithms for Mining Emerging Patterns; 3.3.1 Border-Based Algorithms; 3.3.2 Constraint-Based Algorithms; 3.3.3 Tree-Based Algorithms; 3.3.4 Evolutionary FuzzyIntro; Acknowledgments; Contents; 1 Introduction to Supervised Descriptive Pattern Mining; 1.1 Patterns in Data Analysis; 1.2 Pattern Mining: Types of Patterns and Advanced Data Types; 1.2.1 Frequent/Infrequent Patterns; 1.2.2 Positive/Negative Patterns; 1.2.3 Maximal/Closed/Colossal Patterns; 1.2.4 Condensed Patterns; 1.2.5 Patterns on Advanced Data Types; 1.2.5.1 Data Streams; 1.2.5.2 Sequential Data; 1.2.5.3 Spatiotemporal Data; 1.2.5.4 Graphs; 1.2.5.5 High Utility Data; 1.2.5.6 Uncertain Data; 1.2.5.7 Multiple-Instance Data; 1.3 Supervised Descriptive Patterns; 1.3.1 Contrast Sets 1.3.2 Emerging Patterns1.3.3 Subgroup Discovery; 1.3.4 Class Association Rules; 1.3.5 Exceptional Models; 1.3.6 Other Forms of Supervised Descriptive Patterns; 1.4 Scalability Issues; 1.4.1 Heuristic Approaches; 1.4.2 New Data Structures; 1.4.3 Parallel Computing; 1.4.4 MapReduce Framework; References; 2 Contrast Sets; 2.1 Introduction; 2.2 Task Definition; 2.2.1 Quality Measures; 2.2.2 Tree Structures; 2.2.2.1 Frequent Tree Ordering; 2.2.2.2 Difference Ordering; 2.2.2.3 Hybrid Ordering; 2.3 Algorithms for Mining Contrast Sets; 2.3.1 STUCCO; 2.3.2 CIGAR; 2.3.3 CSM-SD 2.3.4 Additional ApproachesReferences; 3 Emerging Patterns; 3.1 Introduction; 3.2 Task Definition; 3.2.1 Problem Decomposition; 3.2.2 Types of Emerging Patterns; 3.3 Algorithms for Mining Emerging Patterns; 3.3.1 Border-Based Algorithms; 3.3.2 Constraint-Based Algorithms; 3.3.3 Tree-Based Algorithms; 3.3.4 Evolutionary Fuzzy System-Based Algorithms; References; 4 Subgroup Discovery; 4.1 Introduction; 4.2 Task Definition; 4.2.1 Quality Measures; 4.2.1.1 Discrete Target Variables; 4.2.1.2 Numerical Target Variables; 4.2.2 Unifying Related Tasks; 4.3 Algorithms for Subgroup Discovery 4.3.1 Extensions of Classification Algorithms4.3.2 Extensions of Association Rule Mining Algorithms; 4.3.3 Evolutionary Algorithms; 4.3.4 Big Data Approaches; References; 5 Class Association Rules; 5.1 Introduction; 5.2 Task Definition; 5.2.1 Quality Measures; 5.2.2 Class Association Rules for Descriptive Analysis; 5.2.3 Class Association Rules for Predictive Analysis; 5.2.4 Related Tasks; 5.3 Algorithms for Class Association Rules; 5.3.1 Algorithms for Descriptive Analysis; 5.3.2 Algorithms for Predictive Analysis; References; 6 Exceptional Models; 6.1 Introduction; 6.2 Task Definition 6.2.1 Original Model Classes6.2.1.1 Correlation Models; 6.2.1.2 Regression Models; 6.2.1.3 Classification Models; 6.2.2 Rank Correlation Model Class; 6.2.3 Related Tasks; 6.3 Algorithms for Mining Exceptional Models; 6.3.1 Exceptional Model Mining; 6.3.2 Exceptional Preference Mining; 6.3.3 Exceptional Relationship Mining; References; 7 Other Forms of Supervised Descriptive Pattern Mining; 7.1 Introduction; 7.2 Additional Tasks; 7.2.1 Closed Sets for Labeled Data; 7.2.2 Bump Hunting; 7.2.3 Impact Rules; 7.2.4 Discrimination Discovery; 7.2.5 Describing Black Box Models; 7.2.6 Change Mining … (more)
- Publisher Details:
- Cham, Switzerland : Springer Nature
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 006.3/12
Computer science
Data mining
Pattern recognition systems
COMPUTERS / General
Computers -- Intelligence (AI) & Semantics
Computers -- Computer Vision & Pattern Recognition
Artificial intelligence
Pattern recognition
Artificial intelligence
Optical pattern recognition
Computers -- Database Management -- Data Mining
Data mining
Electronic books - Languages:
- English
- ISBNs:
- 9783319981406
3319981404 - Related ISBNs:
- 9783319981390
- Notes:
- Note: Online resource; title from PDF title page (EBSCO, viewed October 10, 2018).
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.337688
- Ingest File:
- 01_285.xml