Periodic Pattern Mining : Theory, Algorithms, and Applications / edited by R. Uday Kiran, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, Anirban Mondal.

Format:

Book

Contributor:

Kiran, R. Uday., Editor.

Fournier-Viger, Philippe, Editor.

Luna, Jose M., Editor.

Lin, Jerry Chun-Wei, Editor.

Mondal, Anirban, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Data mining.

Artificial Intelligence.

Machine Learning.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (VIII, 263 pages) : 65 illustrations, 46 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.

Contents:

Chapter 1: Introduction to Data Mining

Chapter 2: Discovering Frequent Patterns in Very Large Transactional Database

Chapter 3: Discovering Periodic Frequent Patterns in Temporal Databases

Chapter 4: Discovering Fuzzy Periodic Frequent Patterns in Quantitative Temporal Databases

Chapter 5: Discovering Partial Periodic Patterns in Temporal Databases

Chapter 6: Finding Periodic Patterns in Multiple Sequences

Chapter 7: Discovering Self Reliant Patterns

Chapter 8: Finding Periodic High Utility Patterns in Sequence

Chapter 9: Mining Periodic High Utility Sequential Patterns with Negative Unit Profits

Chapter 10: Hiding Periodic High Utility Sequential Patterns

Chapter 11: NetHAPP

Chapter 12: Privacy Preservation of Periodic Frequent Patterns using Sensitive Inverse Frequency.

Other Format:

Printed edition:

ISBN:

978-981-16-3964-7

9789811639647

Access Restriction:

Restricted for use by site license.