May 1, 2024
3 minute read
Types of Pattern Discovery
Pattern discovery may be done on any dataset and with a variety of techniques. Common types of patterns include:
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Cluster Analysis: is identifying subgroups within a given data set.
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Association Analysis: is finding associations or relationships between two or more data items within a larger data set.
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Outlier Detection: is identifying data points that deviate from the overall pattern.
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Sequential Pattern Discovery: is identifying patterns in the order of events or elements.
Applications of Pattern Discovery
Pattern discovery has a wide range of applications in various domains, including:
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Find a path to becoming a Pattern Discovery. Learn more at:
OpenCourser.com/topic/h0l3cr/pattern
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Pattern Discovery.
Provides a comprehensive overview of pattern discovery techniques, including frequent pattern mining, association rule mining, clustering, classification, and anomaly detection. It valuable resource for both students and practitioners who want to learn about and apply pattern discovery methods.
Provides a broad overview of pattern recognition and machine learning, including supervised and unsupervised learning, dimensionality reduction, and model selection. It good choice for students and practitioners who want to gain a solid foundation in these areas.
Focuses on pattern discovery in graphs, including graph clustering, classification, and anomaly detection. It good choice for researchers and practitioners who work with graph data.
Focuses on pattern discovery in spatial data, including spatial clustering, classification, and anomaly detection. It good choice for researchers and practitioners who work with spatial data.
Provides a theoretical foundation for pattern discovery, including topics such as data structures, algorithms, and complexity analysis. It good choice for researchers and practitioners who want to develop new pattern discovery algorithms.
Focuses on pattern discovery in high-dimensional data, including clustering, classification, and anomaly detection. It good choice for researchers and practitioners who work with high-dimensional data.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/h0l3cr/pattern
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