Data Mining
- Authors
This video introduces data mining as a collection of computational methods for extracting knowledge from large digital datasets. It outlines the main stages of a data mining workflow - preprocessing and analysis - and explains key techniques including clustering, outlier detection, classification, and association pattern mining, with examples from humanities research.
Maciej Janicki is a visiting scholar in Digital Humanities at the University of Helsinki. He is an expert in Machine Learning, NLP and text mining.
This introductory video is part of a video library used as teaching resource for BA Course Introduction to Helsinki Digital Humanities and Social Science at the University of Helsinki.
Learning Outcomes
After watching this video, learners will be able to:
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Define data mining and explain its purpose
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Describe the main stages of a data mining workflow, including feature extraction, data cleaning, and feature selection
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Distinguish between the four core data mining tasks: clustering, outlier detection, classification, and association pattern mining
