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This course introduces fundamental concepts, technologies, and applications of data mining techniques. This course organizes lectures from two aspects: 1) It covers the state-of-the-art topics in data mining including: data collection like web scraping, data process like data process and feature engineering, essential data mining techniques like similarity, unsupervised learning techniques such as frequent itemset mining, and supervised learning such as Decision Tree, Naïve Bayes Model and KNN. In addition, advanced topics such as ensemble learning will also be discussed. 2) It will introduce the techniques of mining special types of data which are increasingly common, including text, and data streams.
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