BOOK-CHAPTER

Frequent Pattern Mining

Takeaki UnoWagner Meira, Jr

Year: 2020 Cambridge University Press eBooks Pages: 217-218   Publisher: Cambridge University Press

Abstract

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

Keywords:
Computer science Cluster analysis Artificial intelligence Data science Analytics Machine learning Data stream mining Artificial neural network Data mining Focus (optics) Probabilistic logic

Metrics

2
Cited By
0.45
FWCI (Field Weighted Citation Impact)
5
Refs
0.53
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Data Mining Algorithms and Applications
Physical Sciences →  Computer Science →  Information Systems
Rough Sets and Fuzzy Logic
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Algorithms and Data Compression
Physical Sciences →  Computer Science →  Artificial Intelligence

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