mining weighted sequential patterns in a sequence

8/8/2019 Mining Weighted Sequential Patterns in a Sequence

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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

LVWKHEDVHQRWRGHWHUPLQHWKHDPRXQWRIZHLJKWUHGXFWLRQSHUXQLWWLPH


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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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6'%0LQVXSWLPHLQWHUYDOZHLJKWLQJ

IXQFWLRQ

SV7L:6

6FDQ6'%RQFH)RUHDFKVHTXHQFH6FDOO*HW:HLJKW6)LQGHDFKWLPHLQWHUYDOIUHTXHWLWHPVXFKWKDW7L:66XSS!PLQ6XSS

)RUHDFKWLPHLQWHUYDOZHLJKWHGIUHTXHQWLWHPRXWSXW DQGFDOO6SDQO6O

7KHFRPSOHWHVHWRI7L:6SDWWHUV

,QSXW

3URFHGXUH6SDQO6O

3URFHGXUH*HW:HLJKW6

0LQLQJ7L:6SDWWHUQVLQODUJHVHTXHQFHGDWDEDVH

OVO2XWSXW

6VVVVQ!766WWWWQ!

:6

O6O

7L:6SDWWHUQV7L:6SDWWHUQ6LQJOHLWHP


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3URFHGXUH*HW:HLJKW6

3DUDPHWHUV

6LVDVHTXHQFHZLWKHOHPHQWV766LVLWVWLPHVWDPSOLVWLH6VVVVQ!

766WWWO!/HWZ7,EHDZHLJKWLQJIXQFWLRQIRUDWLPHLQWHUYDO7,

0HWKRG

)RUHDFKSDLURIHOHPHQWVVLDQGVMLMOILQGWKHWLPHLQWHUYDO7,LMEHWZHHQWKHWZRHOHPHQWVDQG

ILQGWKHVWUHQJWKRIWKHSDLU67LM

)LQGWKHWLPHLQWHUYDOZHLJKWRIVZ6DVIROORZV

:6

5HWXUQ:6


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3DUDPHWHUV

LVDVHTXHQWLDOSDWWHUQOLVWKHOHQJWKRI

6OLVSURMHFWHGGDWDEDVH

0HWKRG

6FDQ6O RQFHILQGHDFKWLPHLQWHUYDOZHLJKWHGIUHTXHQWLWHPVXFKWKDWLWFDQEHDVVHPEOHGWRWKHODVWHOHPHQWRIWRIRUPD7L:6SDWWHUQRULWFDQEHDSSHQGHGWRWRIRUPD7L:6SDWWHUQ

)RUHDFKWLPHLQWHUYDOZHLJKWHGIUHTXHQWLWHPDSSHQGLWWRWRIRUPD7L:6SDWWHUQDQGRXWSXW

)RUHDFKFRQVWUXFWSURMHFWHGGDWDEDVHVODQG

FDOOVSDQOVO

3URFHGXUH6SDQO6O


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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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mining weighted sequential patterns in a sequence

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