Mining Infrequent Patterns across Multiple Streams of Data

The problem of extracting infrequent patterns from streams and building relations between them is gradually significant today as many events of interest such as network attacks or unusual stories in news data occur rarely. The complexity of the problem is multipart when a system is required to deal with data from several streams. To discourse these problems we present an approach that combines pyramidal trees with association rule mining to discover infrequent patterns in data streams. This maintains a summary of the data without requiring increasing amounts of memory resources.