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PATTERN SEQUENCE

MINING

Presented By:

DEVIKA MITTAL

O915CS081019

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CONTENTS

Some terminology

association rule

sequential pattern

sequence Database support

What is Sequential Pattern Mining?

Challenges ?Algorithms

Applications

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association rule

the rule can be Buy A=)Buy B.

mining does not take the time stamp into

account,

NOTE:

If we take time stamp into account then we

can get more accurate and useful rules

such as: Buy A implies Buy B within a week,or usually people Buy A every week.

make more sound decisions.

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Sequential pattern

It is a sequence of itemsets that frequently

occurred in a specific order, all items in the

same itemsets are supposed to have the

same transaction time value or within a timegap.

transactions of a customer are together

viewed as a sequence

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Sequence Database

sequence database S is shown with minsupport= 2

set ofitems in the database is {aa,b,c,,d,e,f,g}

A sequence {a,(abc)(ac)d(cf)}

has five elements.

It is also a 9 sequence

since there are 9 instance

in sequence

Sequence Id Sequence

10 {a,(abc)(ac)d(cf)}

20 {(ad)c,(bc)(ae)}

30 {(ef)(ab)(df)cb}

40 {eg(af)cbc}

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Support Support, a customer support a sequence s if

s is contained in the correspondingcustomer-sequence, the support of sequence s is

dened as the fraction of customers whosupport this sequence.

Support(s) = Number of support customers

Total number of customers

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What Is Sequential Pattern

Mining? Given a set of sequences, find the complete set of

frequentsubsequences.

sequential pattern mining is trying to find the

relationships between occurrences of sequential

events, to find if there exist any

specific order of the occurrences.

Sequential pattern mining is the process

of extracting certain sequential patterns

whose support exceed a predefined

minimal support threshold.

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Example..

From a book store's transaction databasehistory, we can find the frequent sequential

purchasing patterns,

for example 80% customers who brought thebook Database Management typically boughtthe book Data Warehouse and then broughtthe book Web Information System with

certain time gap.

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Types:

string mining:

used in biology, to examine gene and proteinsequences

primarily concerned with sequences with a singlemember at each position.

Itemset mining: used more often in marketing

concerned with multiple-symbols at each position.

popular approach to text mining.

http://en.wikipedia.org/wiki/Genehttp://en.wikipedia.org/wiki/Proteinhttp://en.wikipedia.org/wiki/Text_mininghttp://en.wikipedia.org/wiki/Text_mininghttp://en.wikipedia.org/wiki/Proteinhttp://en.wikipedia.org/wiki/Gene

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Challenges on Sequential Pattern

Mining A huge number of possible sequential patterns are

hidden in databases

A mining algorithm should

find the complete set of patterns, whenpossible, satisfying the minimum support

(frequency) threshold

be highly efficient, scalable, involving only asmall number of database scans

be able to incorporate various kinds ofuser-

specific constraints

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Sequential Pattern Mining

Algorithms Apriori-based Approaches

GSP

SPADE

sequential pattern mining methodsfollow the

methodology of Apriori encounters problems when a sequence

database is large Pattern-Growth-based Approaches

FreeSpan PrefixSpan

substantially reduces the size of projected databases and leadsto efficient processing.

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Applications

Applications of sequential pattern mining

Customer shopping sequences: 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.

Telephone calling patterns, Weblog click streams

DNA sequences and gene structures

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CONCLUSION:

Still more improvements are likely to be done.

Balance and more clarity for results.

More research is needed.

In essence, the database need a way to store

more pages, combat data, and still provide (or

attempt to provide) pertinent results.

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THANK YOU

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ANY QUERY..???