Mining Closed Multi-Dimensional Interval Patterns
Date Issued
2010
Date
2010
Author(s)
Lee, Wei-Cheng
Abstract
Many methods have been proposed to find frequent one-dimensional (1-D) interval patterns, where each event in the database is realized by a 1-D interval. However, the events in many applications are in nature realized by multi-dimensional intervals, such as systolic pressure, diastolic pressure, and pulse in medical treatment analysis, where each index during a certain period of time may be represented by a 1-D interval. Therefore, in this thesis, we propose an efficient algorithm, called MIAMI, to mine closed multi-dimensional interval patterns from a database. The MIAMI algorithm employs a pattern tree to enumerate all frequent patterns and generates the patterns in a depth-first search manner. In the mining process, we employ several effective pruning strategies to remove impossible patterns and perform a closure checking scheme to eliminate non-closed patterns. The experimental results show that the MIAMI algorithm is more efficient and scalable than the modified Apriori algorithm.
Subjects
multi-dimension interval pattern
1-dimension interval pattern
frequent pattern
closed pattern
data mining
Type
thesis
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