MIS372 Lecture Notes - Lecture 1: Sequential Pattern Mining, Data Mining, Cluster Analysis
1 Oct 2018
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Data mining and its types: data mining. Complex questions that require more than traditional statistical analyses: two main categories: Focus on historical data only and summarises what has happened. / what were the airlines with the highest passenger satisfaction rates in 2015-17?) Extracts or presents the main descriptive features from data. Summarises data w. r. t. specific data dimensions (e. g. time, manufacturer, product, event, demographics, interests and the similar) Make no assumptions about data prior to modelling. Association rule mining and sequential pattern mining. Focus on future outcomes for the business given their historic data. It will help understand what is likely to occur in the future (e. g. what is the likely student retention rate in course x in my university next year?) Models existing historic data to be able to predict likely future outcomes or events given similar future unseen data, for this predictive analysis creates a model that represents how different variables in data are related to each other.
