Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of ...
Pushing Convertible Constraints in Frequent Itemset Mining
Recent work has highlighted the importance of the constraint-based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. Constraint pushing ...
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Web servers keep track of web users' browsing behavior in web logs. From these logs, one can build statistical models that predict the users' next requests based on their current behavior. These data are complex due to their large size and sequential ...
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning ...