Infrequent Item Mining in Multiple Data Streams
The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the prob- lem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based associa- tion mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques.