Clustering Data with Oracle Data Mining and Oracle Business Intelligence

Presented by Tim Vlamis

Data clustering models are among the most common and most useful predictive analytic techniques and are fundamental to understanding complex and highly dimensioned datasets. They’re perfect for segmenting customers and markets, identifying hidden patterns when businesses have hundreds of locations, and organizing products in new ways. We’ll review OBI 12c and Oracle Data Visualization’s new automatic *Cluster* function and show demonstrate the concepts behind clustering. Learn the differences between Oracle Data Mining three clustering algorithms, K-Means, O-Cluster, and Expectation Maximization and when to use each. You’ll hear how to interpret the hierarchical structure of Oracle’s clustering algorithms and explain the results of clustering models to executives and other lay audiences. This session will be a combination of lecture and live demo of Oracle Data Mining using SQL Developer’s Data Miner extension and BICS. Because clustering algorithms are an unsupervised data mining technique, their analytic workflows are some of the simplest to build and the results are some of the easiest to explain. Many analysts want to use predictive analytics but are unsure where or how to start. This session offers an easy first step that has great application for nearly every business. If you’ve always wanted to do predictive analytics but weren’t sure where to start, come learn how to leverage Oracle’s Advanced Analytics option and get going.

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