Infrequent Item mining in multiple data streams
MetadataShow full item record
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 problem 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 association 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.
Showing items related by title, author, creator and subject.
Techniques for improving clustering and association rules mining from very large transactional databasesLi, Yanrong (2009)Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have ...
Mohania, M.; Dhruv, S.; Gupta, S.; Bhowmick, S.; Dillon, Tharam S. (2005)There has been a rising need to handle and process streaming kind of data. It is continuous, unpredictable, time-varying in nature and could arrive in multiple rapid streams. Sensor data, web clickstreams, etc. are the ...
Gender, grade-level and stream differences in learning environment and student attitudes in primary science classrooms in SingaporePeer, Jarina (2011)A major focus of this research was the validity and reliability of a learning environment and attitude questionnaire in primary school classrooms in Singapore. The learning environment scales were chosen from Constructivist ...