Clusters are clumps of data that are internally cohesive and separated from other clusters. In marketing disciplines, cluster analysis is the basis for identifying clusters of customer records, a process call market segmentation. An anomaly is a pattern in the data that does not conform to expected normal behavior. In one sense an anomaly is the flip side of a cluster: a data point, or points that are distant from a cluster. Anomaly detection is useful in a variety of fields (surveillance for fraud, monitoring of complex industrial processes, to name two). This is a hands-on course in which you will use statistical software to apply cluster method algorithms to real data, and interpret the results. This same cluster analysis can be used to identify anomalies. The course also covers the use of supervised learning algorithms to identify anomalies.
Mr. Anthony Babinec
Anthony Babinec is the President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President.
