Tutorials
A tutorial is a method of transferring knowledge.
More interactive and specific than a lecture, a tutorial seeks to teach by example and supply the information (practical and theoretical) to complete a certain task.
Analysis of Data Streams
Tutorial Overview
The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams.
Content
– IoT Fundamentals and Stream Mining Algorithms
– IoT Stream mining setting
– Clustering
– Classification and Regression
– Concept drift
– Frequent Pattern mining
Categorical Data Analysis and VisualiZation
Tutorial Overview
The impact of the internet, social media and smart devices means that people are becoming increasingly literate with use of these technologies and it has changed how we engage with others on a professional and personal level. For the analyst, the capacity to adapt to such changes has impacted upon the tools designed for analyzing “big data”, i.e. huge amount of numerical and categorical data. One of the most important tools is that of “visualization”.
With focus on categorical data, this tutorial, after briefly introducing association indices, models and methods, will outline some cutting-edge visualization tools and techniques.
Content
– A Quick Historical Overview of the Visualization of Categorical Data
– The Contingency Table and the Chi-Squared Statistic
– Measures of Symmetric Association for I x J Contingency Tables
– Measures of Asymmetric Association for I x J Contingency Tables
– Correspondence Analysis (symmetrical, non-symmetrical, ordinal)
– Multiple and Multi-way Correspondence Analysis