عنوان

High-utility pattern mining :

(Number (ISBN

3030049213

(Number (ISBN

3030049221

(Number (ISBN

9783030049218

(Number (ISBN

9783030049225

Erroneous ISBN

3030049205

Erroneous ISBN

9783030049201

Title Proper

High-utility pattern mining :

General Material Designation

[Book]

Other Title Information

theory, algorithms and applications /

First Statement of Responsibility

editors, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Roger Nkambou, Bay Vo and Vincent S. Tseng.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2019]

Specific Material Designation and Extent of Item

1 online resource

Series Title

Studies in big data,

Volume Designation

volume 51

ISSN of Series

2197-6511 ;

Text of Note

Includes bibliographical references.

Text of Note

Intro; Preface; Contents; A Survey of High Utility Itemset Mining; 1 Introduction; 2 Problem Definition; 2.1 Frequent Itemset Mining; 2.2 High Utility Itemset Mining; 2.3 Key Properties of the Problem of High Utility Itemset Mining; 3 Algorithms; 3.1 Two Phase Algorithms; 3.2 One Phase Algorithms; 3.3 A Comparison of High Utility Itemset Mining Algorithms; 4 Extensions of the Problem; 4.1 Concise Representations of High Utility Itemsets; 4.2 Top-k High Utility Itemset Mining; 4.3 High Utility Itemset Mining with the Average Utility Measure

Text of Note

3 Approaches to Top-K High Utility Itemset Mining3.1 Two-Phase Methods; 3.2 One-Phase Methods; 4 Performance Analysis of State-of-the-Art Top-K HUI Mining Methods; 4.1 Experimental Design; 4.2 Experimental Results; 5 Top-K High Utility Pattern Mining Variants; 6 Open Issues and Future Research Opportunities; 7 Conclusions; References; A Survey of High Utility Pattern Mining Algorithms for Big Data; 1 Introduction; 2 High Utility Pattern Mining: Overview; 2.1 Overview of Pattern Mining Methodologies; 3 Overview of Big Data Paradigms; 3.1 Parallel Processing; 3.2 Distributed Platforms

Text of Note

3.1 Mining High Utility-Probability Sequential Patterns in Uncertain Databases3.2 High-Utility Sequential Pattern Mining with Multiple Minimum Utility Thresholds; 3.3 Top-k High Utility Sequential Pattern Mining; 3.4 Mining Periodic High Utility Sequential Patterns; 3.5 Related Problems; 4 Research Opportunities; 5 Conclusion; References; Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation; 1 Introduction; 2 Background; 2.1 Preliminaries; 2.2 Related Work; 3 Mining High Utility Itemsets; 3.1 Utility-List Structure; 3.2 The Proposed Method: HUI-Miner

Text of Note

3.3 Data Stream Mining4 Scalable and Parallel High Utility Itemset Mining; 4.1 Scalable Serial Processing; 4.2 Distributed and Parallel Processing; 5 High Utility Sequential Pattern Mining; 5.1 Serial Processing; 5.2 Distributed and Parallel Processing; 6 Conclusions and Future Directions; References; A Survey of High Utility Sequential Pattern Mining; 1 Introduction; 2 Problem Definition and Algorithm; 2.1 Definition of the High Utility Sequential Pattern Mining Problem; 2.2 Upper Bounds on umax and their Key Properties; 2.3 Algorithms; 3 Extensions of the Problem

Text of Note

4.4 High Utility Itemset Mining with Negative Utilities4.5 High Utility Itemset Mining with Discount Strategies; 4.6 Mining High Utility Itemset with a Maximum Length Constraint; 4.7 Mining High Utility Itemsets that Are Correlated; 4.8 Periodic High Utility Itemset Mining; 4.9 On-Shelf High Utility Itemset Mining; 4.10 High Utility Itemset Mining in Dynamic Databases; 4.11 Other Extensions; 5 Research Opportunities; 6 Open-Source Implementations; 7 Conclusion; References; A Comparative Study of Top-K High Utility Itemset Mining Methods; 1 Introduction; 2 Preliminaries and Problem Statement

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Text of Note

This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030049218

Title

High-utility pattern mining.

International Standard Book Number

9783030049201

Big data.

Data mining.

Big data.

COMPUTERS-- General.

Data mining.

COM-- 000000

UYQ

UYQ

Number

Edition

Class number

Fournier-Viger, Philippe

Lin, Jerry Chun-Wei

Nkambou, Roger,1966-

Tseng, Vincent S.

Vo, Bay

Date of Transaction

20200823081046.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y