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Mining weighted sequential patterns in a
sequence database with a time-interval weight
Author:Joong Hyuk Chang
DCIT, Daegu University,
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ABSTRACT
The weighted sequential pattern mining aims to find more interesting
sequential patterns, considering the different significance of each data
element in a sequence database.
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OUTLINE
ABSTRACT
KEYWORDS
APPLICATIONS
INTRODUCTION
RELATED WORK PROBLEM DEFINITION
TiWS Patterns
Mining TiWS patterns in a large database
EXPERIMENTAL RESULTS
CONCLUSION
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KEY WORDS
Sequence database
Time-interval sequence database
Sequential pattern mining Weighted sequential pattern
TiWS pattern
TiWS support
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KEY WORDS
Sequence database: A sequence database consists of ordered
elements or events.
Time-interval sequence database: The sequence database with
associated time stamp list.
Sequential pattern mining: Given a set of sequences and supportthreshold, finding the complete set of frequent subsequences.
Given support thresholdmin_sup =2, is a sequentialpattern.
Weighted sequential pattern mining: The sequentional patternmining of the weighted sequences in a sequence database.
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APPLICATIONS
Applications of sequential pattern mining
Customer purchase pattern analysis
First buy computer, then CD-ROM, and then digital camera,
within 3 months.
Medical treatments, natural disasters (e.g., earthquakes), science
& eng. processes, stocks and markets, etc.
Web access pattern analysis
Telephone calling patterns, Weblog click streams
DNA sequences and gene structures analysis.
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INTRODUCTION
Sequential pattern mining aims to discover more
interesting patterns in a sequence database.
Example:
[Customer_A]:Laser printer______________Jan
Scanner_________________Feb
CD Burner_______________March
[Customer_B]:
Laser printer______________Jan
Scanner_________________Jun
CD Burner_______________Sep.
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RELATED WORK
To improve the usefulness of mining results in realworld applications, weighted pattern mining has beenstudied in association rule mining and sequentialpattern mining .Most of the weighted pattern mining
algorithms usually require pre-assigned weights, andthe weights are generally derived from the quantitativeinformation and the importance of items in a real worldapplication.
General sequential pattern mining,
Closed and maximal sequential pattern mining, etc.
SPADE and PrefixSpan are more efficient in terms ofprocessing time.
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PROBLEM DEFINITION
Let I={i1,i2,in} be a set of all items.
A sequence S= is an ordered list ofitemsets,
where sj : itemset, Time stamp list TS(S)=
where tj-1
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Subsequence vs. super sequence
Given two sequences =< a1 a2 an > and =< b1 b2 bm >
is called a subsequence of , denoted as , if thereexist integers 1 j1 < j2 and =< (abc), (de)>
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TiWS-patterns
A time-interval between pair of itemsets:
Time interval weight of a pair of itemsets.
Time interval weight of a sequence.1. Strength of a pair of itemsets.
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A time-interval between pair of items
Definition 1:A time-interval between pair of items:S= is a sequence.TS(S)=be the time stamp list
The time interval between si and sj isTiij=tj-ti where(1
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Time interval weight of a pair of itemsets.
3 weight functions as
WF_1:General scale weighting: Wg(TIij)=7,
LMXWMWLX
WF_2:Log scale weighting:Wl(TIij)=ORJ7,LMXORJWM
WLX
WF_3:General scale weighting with a
ceiling:Wc(TIij)=7,LM
X
WMWL
X
:KHUHXX!WKHVL]HRIWKHXQLWWLPHDQG
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Time ineterval weight of the sequence.
Definition 2:
Strength of a pair of itemsets
STij=length(si)xlength(sj).
Time-interval weight of a sequence.
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TiWS-Support
Definition3:(TiW-support of a sequence)
The TiW-support of a sequence X in SDB,TiW-Supp(X), is defined as follows
WKHZHLJKWRIWKHVHTXHQFH;7KHZHLJKWRIWKHWRWDOVHTXHQFHVLQ6'%
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TiWS-Support
Definition 6:TiWS-patterns
Given a support threshold minSupport(0
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Anti-monotone property of TiWS-support
Let A and B be sequences in an SDB, and B is a supersequence of A then the TiWS-support of A is found as
VLQFH$%WKHZHLJKWRIWKH$LVDOZD\VJUHDWHUWKDQWKHZHLJ $FFRUGLQJO\WKHIROORZLQJKROGV
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CONCLUSION
A process to get the weight of the sequence isproposed.
A new framework is developed for miningweighted sequential patterns.
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