Knowledge Discovery and Data Mining FYP Presentation Examples

Project Title	Project Description	Link(s)
2016 LIX3 Finding Insights from MOOC Forum Dynamics	Data scraping, visualization and analysis of large Coursera datasets to gain interesting and intuitive insights concerning the students' motivation, their activeness in forum activities and the influence among active users
2015 RAYW1 Learning Social Influence from Past Data	A new approach to calculate the probabilities of influence of one social networking user over another user from the perspective of the person who observed the actions (rather than from the perspective of the user who performs the actions)
2015 RAYW3 Binary Classification with Models and Data Density Distribution	Classification based on probabilistic data rather than deterministic data (with 0-1 labels)
2012 RAYW1 Data Mining for Business Applications	Using statistical methods to find people most likely to help with viral marketing
2012 RAYW3 Modeling for Data Mining in a Social Network	Viral marketing tool with a nice GUI
2012 RO4 Social Investment Forecasting Using Web Mining	Nice GUI, creative use of social networking data
2011 MA3 Texas Holdem Poker AI Utilizing Machine Learning Techniques	Experimenting with different machine learning paradigms to find an improved means to calculate win probabilities (Only the last 32 minutes were recorded due to an early start.)
2011 RAYW3 Using Knowledge Discovery to Generate Melodies for New Chinese Lyrics	Application of data mining techniques to extract frequent patterns and frequent rhythms from pop songs; distinguished project!
2010 WIL3 Visualizing User Preferences via Advanced Data Mining Techniques	Cool GUI, difficult algorithm and well-rehearsed verbal presentation

Knowledge discovery focuses on the process of extracting meaningful patterns from big data, and data mining uses automated computational and statistical tools and techniques on big data. Together, their underlying goal is to help humans make high-level sense of large volumes of low-level data, and share that knowledge with people in related fields, especially in the areas of business and politics. It can involve methods for data preparation, cleaning, and selection, use of appropriate prior knowledge, development and application of data mining algorithms, and proper results analysis. It engages methods from such diverse areas as machine learning, pattern recognition, database science, statistics and analytics, artificial intelligence, knowledge acquisition for expert systems, data modeling and visualization, and high performance computing.

Knowledge Discovery and Data Mining FYP Presentation Examples

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