Paper Info
Title
Finding Interesting Sequential Patterns In Sequence Data Streams Via A Time-Interval Weighting Approach
Abstract
The mining problem over data streams has recently been attracting considerable attention thanks to the usefulness of data mining in Various application fields of information science, and sequence data streams are so common in daily life. Therefore, a study on mining sequential patterns over sequence data streams can give valuable results for wide use in various application fields. This paper proposes a new framework for mining novel interesting sequential patterns over a sequence data stream and a mining method based on the framework. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders. The proposed framework is capable of obtaining more interesting sequential patterns over sequence data streams whose data elements are highly correlated in terms of generation time.
Year
DOI
Venue
2013
10.1587/transinf.E96.D.1734
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
time-interval weight, weighted sequential pattern, time-interval sequential pattern, time-interval sequence data stream, data stream
Data mining,Weighting,Pattern recognition,Computer science,Data stream,Data sequences,Artificial intelligence,STREAMS
Journal
Volume
Issue
ISSN
E96D
8
1745-1361
Citations 
PageRank 
References 
0
0.34
18
Authors
2
Name
Order
Citations
PageRank
Joong Hyuk Chang140119.81
Nam Hun Park21057.63