Finding Constrained Frequent Episodes Using Minimal Occurrences

Finding Constrained Frequent Episodes Using Minimal Occurrences

Recurrent combinations of events within an event sequence, known as episodes, often reveal useful information. Most of the proposed episode mining algorithms adopt an apriori-like approach that generates candidates and then calculates their support levels. Obviously, such an approach is computationa...

Full description

Saved in:
Bibliographic Details
Main Authors: MA, Xi, PANG, Hwee Hwa, TAN, Kian-Lee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
Subjects:
constrained frequent episode
episode mining
episode prefix tree
minimal occurrences
position pairs set
prefix-growth approach
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/1140
https://ink.library.smu.edu.sg/context/sis_research/article/2139/viewcontent/findingConstrainedFrequent_edited_.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:Recurrent combinations of events within an event sequence, known as episodes, often reveal useful information. Most of the proposed episode mining algorithms adopt an apriori-like approach that generates candidates and then calculates their support levels. Obviously, such an approach is computationally expensive. Moreover, those algorithms are capable of handling only a limited range of constraints. In this paper, we introduce two mining algorithms - episode prefix tree (EPT) and position pairs set (PPS) - based on a prefix-growth approach to overcome the above limitations. Both algorithms push constraints systematically into the mining process. Performance study shows that the proposed algorithms run considerably faster than MINEPI (Mannila and Toivonen, 1996).

Similar Items