This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder鈥檚 MS in Data Science or MS in Computer Science degrees offered on the 糖心vlog官网观看 platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on 糖心vlog官网观看 are ideal for recent graduates or working professionals. Learn more: MS in Data Science: /degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Skills You'll Learn
Advanced Analytics, Algorithms, Data Analysis, Data Mining, Supervised Learning, Machine Learning Methods, Anomaly Detection, Classification And Regression Tree (CART), Unsupervised Learning, Artificial Neural Networks, Exploratory Data Analysis, Data Science, Predictive Modeling, Unstructured Data, Statistical Machine Learning, Text Mining, Big Data, Machine Learning Algorithms, Statistical Analysis
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From the lesson
Frequent Pattern Analysis
This week starts with an overview of this course, Data Mining Methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis.
Taught By
Qin (Christine) Lv
Associate Professor