Docsity
Docsity

Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity


Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan


Guidelines and tips
Guidelines and tips

data warehouse and architecture, Lecture notes of Data Mining

data warehouse and architecture

Typology: Lecture notes

2018/2019

Uploaded on 11/04/2019

priya-mohan-2
priya-mohan-2 🇮🇳

2 documents

1 / 9

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
1
,QWURGXFWLRQWR'DWD:DUHKRXVHV
DQG2/$37HFKQRORJLHV
Data Mining Lecture 2 2
2YHUYLHZ
:KDWLVDGDWDZDUHKRXVH"
$PXOWLGLPHQVLRQDOGDWDPRGHO
'DWDZDUHKRXVHDUFKLWHFWXUH
'DWDZDUHKRXVHLPSOHPHQWDWLRQ
)XUWKHUGHYHORSPHQWRIGDWDFXEHWHFKQRORJ\
)URPGDWDZDUHKRXVLQJWRGDWDPLQLQJ
Data Mining Lecture 2 3
:KDWLV'DWD:DUHKRXVH"
'HILQHGLQPDQ\GLIIHUHQWZD\VEXWQRWULJRURXVO\
² $GHFLVLRQVXSSRUWGDWDEDVHWKDWLVPDLQWDLQHGVHSDUDWHO\
IURPWKHRUJDQL]DWLRQ·VRSHUDWLRQDOGDWDEDVH
² 6XSSRUWLQIRUPDWLRQSURFHVVLQJE\SURYLGLQJDVROLGSODWIRUP
RIFRQVROLGDWHGKLVWRULFDOGDWDIRUDQDO\VLV
´$GDWDZDUHKRXVHLVDVXEMHFWRULHQWHGLQWHJUDWHG
WLPHYDULDQWDQGQRQYRODWLOHFROOHFWLRQRIGDWDLQ
VXSSRUWRIPDQDJHPHQW·VGHFLVLRQPDNLQJSURFHVVµ
³:+,QPRQ
'DWDZDUHKRXVLQJ
² 7KHSURFHVVRIFRQVWUXFWLQJDQGXVLQJGDWDZDUHKRXVHV
Data Mining Lecture 2 4
'DWD:DUHKRXVH³6XEMHFW2ULHQWHG
2UJDQL]HGDURXQGPDMRUVXEMHFWVVXFKDVFXVWRPHU
SURGXFWVDOHV
)RFXVLQJRQWKHPRGHOLQJDQGDQDO\VLVRIGDWDIRU
GHFLVLRQPDNHUVQRWRQGDLO\RSHUDWLRQVRUWUDQVDFWLRQ
SURFHVVLQJ
3URYLGHDVLPSOHDQGFRQFLVHYLHZDURXQGSDUWLFXODU
VXEMHFWLVVXHVE\H[FOXGLQJGDWDWKDWDUHQRWXVHIXOLQ
WKHGHFLVLRQVXSSRUWSURFHVV
Data Mining Lecture 2 5
'DWD:DUHKRXVH³,QWHJUDWHG
&RQVWUXFWHGE\LQWHJUDWLQJPXOWLSOHKHWHURJHQHRXV
GDWDVRXUFHV
² UHODWLRQDOGDWDEDVHVIODWILOHVRQOLQHWUDQVDFWLRQUHFRUGV
'DWDFOHDQLQJDQGGDWDLQWHJUDWLRQWHFKQLTXHVDUH
DSSOLHG
² (QVXUHFRQVLVWHQF\LQQDPLQJFRQYHQWLRQVHQFRGLQJ
VWUXFWXUHVDWWULEXWHPHDVXUHVHWFDPRQJGLIIHUHQWGDWD
VRXUFHV
(J+RWHOSULFHFXUUHQF\WD[EUHDNIDVWFRYHUHGHWF
² :KHQGDWDLVPRYHGWRWKHZDUHKRXVHLWLVFRQYHUWHG
Data Mining Lecture 2 6
'DWD:DUHKRXVH³7LPH9DULDQW
7KHWLPHKRUL]RQIRUWKHGDWDZDUHKRXVHLV
VLJQLILFDQWO\ORQJHUWKDQWKDWRIRSHUDWLRQDOV\VWHPV
² 2SHUDWLRQDOGDWDEDVHFXUUHQWYDOXHGDWD
² 'DWDZDUHKRXVHGDWDSURYLGHLQIRUPDWLRQIURPDKLVWRULFDO
SHUVSHFWLYHHJSDVW\HDUV
(YHU\NH\VWUXFWXUHLQWKHGDWDZDUHKRXVH
² &RQWDLQVDQHOHPHQWRIWLPHH[SOLFLWO\RULPSOLFLWO\
² %XWWKHNH\RIRSHUDWLRQDOGDWDPD\RUPD\QRWFRQWDLQ´WLPH
HOHPHQWµ
pf3
pf4
pf5
pf8
pf9

Partial preview of the text

Download data warehouse and architecture and more Lecture notes Data Mining in PDF only on Docsity!

,QWURGXFWLRQWR'DWD:DUHKRXVHV

DQG2/$37HFKQRORJLHV

Data Mining Lecture 2 2

2YHUYLHZ

á :KDWLVDGDWDZDUHKRXVH"

á $PXOWLGLPHQVLRQDOGDWDPRGHO

á 'DWDZDUHKRXVHDUFKLWHFWXUH

á 'DWDZDUHKRXVHLPSOHPHQWDWLRQ

á )XUWKHUGHYHORSPHQWRIGDWDFXEHWHFKQRORJ\

á )URPGDWDZDUHKRXVLQJWRGDWDPLQLQJ

Data Mining Lecture 2 3

:KDWLV'DWD:DUHKRXVH"

á 'HILQHGLQPDQ\GLIIHUHQWZD\VEXWQRWULJRURXVO\

≤ $GHFLVLRQVXSSRUWGDWDEDVHWKDWLVPDLQWDLQHGVHSDUDWHO\
IURPWKHRUJDQL]DWLRQ∑VRSHUDWLRQDOGDWDEDVH
≤ 6XSSRUWLQIRUPDWLRQSURFHVVLQJE\SURYLGLQJDVROLGSODWIRUP
RIFRQVROLGDWHGKLVWRULFDOGDWDIRUDQDO\VLV

á ¥$GDWDZDUHKRXVHLVDVXEMHFWRULHQWHGLQWHJUDWHG

WLPHYDULDQWDQGQRQYRODWLOHFROOHFWLRQRIGDWDLQ

VXSSRUWRIPDQDJHPHQW∑VGHFLVLRQPDNLQJSURFHVVμ

≥:+,QPRQ

á 'DWDZDUHKRXVLQJ

≤ 7KHSURFHVVRIFRQVWUXFWLQJDQGXVLQJGDWDZDUHKRXVHV

Data Mining Lecture 2 4

'DWD:DUHKRXVH≥6XEMHFW2ULHQWHG

á 2UJDQL]HGDURXQGPDMRUVXEMHFWVVXFKDVFXVWRPHU

SURGXFWVDOHV

á )RFXVLQJRQWKHPRGHOLQJDQGDQDO\VLVRIGDWDIRU

GHFLVLRQPDNHUVQRWRQGDLO\RSHUDWLRQVRUWUDQVDFWLRQ

SURFHVVLQJ

á 3URYLGHDVLPSOHDQGFRQFLVHYLHZDURXQGSDUWLFXODU

VXEMHFWLVVXHVE\H[FOXGLQJGDWDWKDWDUHQRWXVHIXOLQ

WKHGHFLVLRQVXSSRUWSURFHVV

Data Mining Lecture 2 5

'DWD:DUHKRXVH≥,QWHJUDWHG

á &RQVWUXFWHGE\LQWHJUDWLQJPXOWLSOHKHWHURJHQHRXV

GDWDVRXUFHV

≤ UHODWLRQDOGDWDEDVHVIODWILOHVRQOLQHWUDQVDFWLRQUHFRUGV

á 'DWDFOHDQLQJDQGGDWDLQWHJUDWLRQWHFKQLTXHVDUH

DSSOLHG

≤ (QVXUHFRQVLVWHQF\LQQDPLQJFRQYHQWLRQVHQFRGLQJ
VWUXFWXUHVDWWULEXWHPHDVXUHVHWFDPRQJGLIIHUHQWGDWD
VRXUFHV

á (J+RWHOSULFHFXUUHQF\WD[EUHDNIDVWFRYHUHGHWF ≤ :KHQGDWDLVPRYHGWRWKHZDUHKRXVHLWLVFRQYHUWHG

Data Mining Lecture 2 6

'DWD:DUHKRXVH≥7LPH9DULDQW

á 7KHWLPHKRUL]RQIRUWKHGDWDZDUHKRXVHLV

VLJQLILFDQWO\ORQJHUWKDQWKDWRIRSHUDWLRQDOV\VWHPV

≤ 2SHUDWLRQDOGDWDEDVHFXUUHQWYDOXHGDWD
≤ 'DWDZDUHKRXVHGDWDSURYLGHLQIRUPDWLRQIURPDKLVWRULFDO
SHUVSHFWLYH HJSDVW\HDUV

á (YHU\NH\VWUXFWXUHLQWKHGDWDZDUHKRXVH

≤ &RQWDLQVDQHOHPHQWRIWLPHH[SOLFLWO\RULPSOLFLWO\
≤ %XWWKHNH\RIRSHUDWLRQDOGDWDPD\RUPD\QRWFRQWDLQ¥WLPH

HOHPHQWμ

Data Mining Lecture 2 7

'DWD:DUHKRXVH≥1RQ9RODWLOH

á $SK\VLFDOO\VHSDUDWHVWRUHRIGDWDWUDQVIRUPHG

IURPWKHRSHUDWLRQDOHQYLURQPHQW

á 2SHUDWLRQDOXSGDWHRIGDWDGRHVQRWRFFXULQWKH

GDWDZDUHKRXVHHQYLURQPHQW

≤ 'RHVQRWUHTXLUHWUDQVDFWLRQSURFHVVLQJUHFRYHU\DQG
FRQFXUUHQF\FRQWUROPHFKDQLVPV
≤ 5HTXLUHVRQO\WZRRSHUDWLRQVLQGDWDDFFHVVLQJ

á LQLWLDOORDGLQJRIGDWDDQGDFFHVVRIGDWD

Data Mining Lecture 2 8

'DWD:DUHKRXVHYV+HWHURJHQHRXV'%

á 7UDGLWLRQDOKHWHURJHQHRXV'%LQWHJUDWLRQ

≤ %XLOGZUDSSHUVPHGLDWRUVRQWRSRIKHWHURJHQHRXVGDWDEDVHV
≤ 4XHU\GULYHQDSSURDFK

á :KHQDTXHU\LVSRVHGWRDFOLHQWVLWHDPHWDGLFWLRQDU\LVXVHG WRWUDQVODWHWKHTXHU\LQWRTXHULHVDSSURSULDWHIRULQGLYLGXDO KHWHURJHQHRXVVLWHVLQYROYHGDQGWKHUHVXOWVDUHLQWHJUDWHGLQWR DJOREDODQVZHUVHW á &RPSOH[LQIRUPDWLRQILOWHULQJFRPSHWHIRUUHVRXUFHV

á 'DWDZDUHKRXVHXSGDWHGULYHQKLJKSHUIRUPDQFH

≤ ,QIRUPDWLRQIURPKHWHURJHQHRXVVRXUFHVLVLQWHJUDWHGLQDGYDQFHDQG
VWRUHGLQZDUHKRXVHVIRUGLUHFWTXHU\DQGDQDO\VLV

Data Mining Lecture 2 9

'DWD:DUHKRXVHYV2SHUDWLRQDO'%

á 2/73 RQOLQHWUDQVDFWLRQSURFHVVLQJ ≤ 0DMRUWDVNRIWUDGLWLRQDOUHODWLRQDO'% ≤ 'D\WRGD\RSHUDWLRQVSXUFKDVLQJLQYHQWRU\EDQNLQJPDQXIDFWXULQJ SD\UROOUHJLVWUDWLRQDFFRXQWLQJHWF á 2/$3 RQOLQHDQDO\WLFDOSURFHVVLQJ ≤ 0DMRUWDVNRIGDWDZDUHKRXVHV\VWHP ≤ 'DWDDQDO\VLVDQGGHFLVLRQPDNLQJ á 'LVWLQFWIHDWXUHV 2/73YV2/$3  ≤ 8VHUDQGV\VWHPRULHQWDWLRQFXVWRPHUYVPDUNHW ≤ 'DWDFRQWHQWVFXUUHQWGHWDLOHGYVKLVWRULFDOFRQVROLGDWHG ≤ 'DWDEDVHGHVLJQ(5DSSOLFDWLRQYVVWDUVXEMHFW ≤ 9LHZFXUUHQWORFDOYVHYROXWLRQDU\LQWHJUDWHG ≤ $FFHVVSDWWHUQVXSGDWHYVUHDGRQO\EXWFRPSOH[TXHULHV Data Mining Lecture 2 10

2/73YV2/$

OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated

historical, summarized, multidimensional integrated, consolidated usage repetitive ad-hoc access read/write index/hash on prim. key

lots of scans unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response

Data Mining Lecture 2 11

:K\6HSDUDWH'DWD:DUHKRXVH"

á +LJKSHUIRUPDQFHIRUERWKV\VWHPV

≤ '%06≥WXQHGIRU2/73DFFHVVPHWKRGVLQGH[LQJ
FRQFXUUHQF\FRQWUROUHFRYHU\
≤ :DUHKRXVH≥WXQHGIRU2/$3FRPSOH[2/$3TXHULHV
PXOWLGLPHQVLRQDOYLHZFRQVROLGDWLRQ

á 'LIIHUHQWIXQFWLRQVDQGGLIIHUHQWGDWD

≤ PLVVLQJGDWD'HFLVLRQVXSSRUWUHTXLUHVKLVWRULFDOGDWDZKLFK
RSHUDWLRQDO'%VGRQRWW\SLFDOO\PDLQWDLQ
≤ GDWDFRQVROLGDWLRQ'6UHTXLUHVFRQVROLGDWLRQ DJJUHJDWLRQ
VXPPDUL]DWLRQ RIGDWDIURPKHWHURJHQHRXVVRXUFHV
≤ GDWDTXDOLW\GLIIHUHQWVRXUFHVW\SLFDOO\XVHLQFRQVLVWHQWGDWD
UHSUHVHQWDWLRQVFRGHVDQGIRUPDWVZKLFKKDYHWREH
UHFRQFLOHG

Data Mining Lecture 2 12

2YHUYLHZ

á :KDWLVDGDWDZDUHKRXVH"

á $PXOWLGLPHQVLRQDOGDWDPRGHO

á 'DWDZDUHKRXVHDUFKLWHFWXUH

á 'DWDZDUHKRXVHLPSOHPHQWDWLRQ

á )XUWKHUGHYHORSPHQWRIGDWDFXEHWHFKQRORJ\

á )URPGDWDZDUHKRXVLQJWRGDWDPLQLQJ

Data Mining Lecture 2 19

$'DWD0LQLQJ4XHU\/DQJXDJH'04/ /DQJXDJH3ULPLWLYHV

á &XEH'HILQLWLRQ )DFW7DEOH

GHILQHFXEHFXEHBQDPH!>GLPHQVLRQBOLVW!@
PHDVXUHBOLVW!

á 'LPHQVLRQ'HILQLWLRQ 'LPHQVLRQ7DEOH

GHILQHGLPHQVLRQGLPHQVLRQBQDPH!DV
DWWULEXWHBRUBVXEGLPHQVLRQBOLVW!

á 6SHFLDO&DVH 6KDUHG'LPHQVLRQ7DEOHV

≤ )LUVWWLPHDV¥FXEHGHILQLWLRQμ ≤ GHILQHGLPHQVLRQGLPHQVLRQBQDPH!DV GLPHQVLRQBQDPHBILUVWBWLPH!LQFXEH FXEHBQDPHBILUVWBWLPH!

Data Mining Lecture 2 20

'HILQLQJD6WDU6FKHPDLQ'04/

GHILQHFXEHVDOHVBVWDU>WLPHLWHPEUDQFKORFDWLRQ@

GROODUVBVROG VXP VDOHVBLQBGROODUV DYJBVDOHV
DYJ VDOHVBLQBGROODUV XQLWVBVROG FRXQW

GHILQHGLPHQVLRQWLPHDV WLPHBNH\GD\GD\BRIBZHHN

PRQWKTXDUWHU\HDU

GHILQHGLPHQVLRQLWHPDV LWHPBNH\LWHPBQDPHEUDQG

W\SHVXSSOLHUBW\SH

GHILQHGLPHQVLRQEUDQFKDV EUDQFKBNH\EUDQFKBQDPH

EUDQFKBW\SH

GHILQHGLPHQVLRQORFDWLRQDV ORFDWLRQBNH\VWUHHWFLW\

SURYLQFHBRUBVWDWHFRXQWU\

Data Mining Lecture 2 21

'HILQLQJD6QRZIODNH6FKHPDLQ'04/

GHILQHFXEHVDOHVBVQRZIODNH>WLPHLWHPEUDQFKORFDWLRQ@

GROODUVBVROG VXP VDOHVBLQBGROODUV DYJBVDOHV
DYJ VDOHVBLQBGROODUV XQLWVBVROG FRXQW

GHILQHGLPHQVLRQWLPHDV WLPHBNH\GD\GD\BRIBZHHN

PRQWKTXDUWHU\HDU

GHILQHGLPHQVLRQLWHPDV LWHPBNH\LWHPBQDPHEUDQG

W\SHVXSSOLHU VXSSOLHUBNH\VXSSOLHUBW\SH

GHILQHGLPHQVLRQEUDQFKDV EUDQFKBNH\EUDQFKBQDPH

EUDQFKBW\SH

GHILQHGLPHQVLRQORFDWLRQDV ORFDWLRQBNH\VWUHHW

FLW\ FLW\BNH\SURYLQFHBRUBVWDWHFRXQWU\

Data Mining Lecture 2 22

'HILQLQJD)DFW&RQVWHOODWLRQLQ'04/

GHILQHFXEHVDOHV>WLPHLWHPEUDQFKORFDWLRQ@

GROODUVBVROG VXP VDOHVBLQBGROODUV DYJBVDOHV DYJ VDOHVBLQBGROODUV  XQLWVBVROG FRXQW GHILQHGLPHQVLRQWLPHDV WLPHBNH\GD\GD\BRIBZHHNPRQWKTXDUWHU \HDU GHILQHGLPHQVLRQLWHPDV LWHPBNH\LWHPBQDPHEUDQGW\SH VXSSOLHUBW\SH GHILQHGLPHQVLRQEUDQFKDV EUDQFKBNH\EUDQFKBQDPHEUDQFKBW\SH GHILQHGLPHQVLRQORFDWLRQDV ORFDWLRQBNH\VWUHHWFLW\SURYLQFHBRUBVWDWH FRXQWU
GHILQHFXEHVKLSSLQJ>WLPHLWHPVKLSSHUIURPBORFDWLRQWRBORFDWLRQ@ GROODUBFRVW VXP FRVWBLQBGROODUV XQLWBVKLSSHG FRXQW GHILQHGLPHQVLRQWLPHDVWLPHLQFXEHVDOHV GHILQHGLPHQVLRQLWHPDVLWHPLQFXEHVDOHV GHILQHGLPHQVLRQVKLSSHUDV VKLSSHUBNH\VKLSSHUBQDPHORFDWLRQDV ORFDWLRQLQFXEHVDOHVVKLSSHUBW\SH GHILQHGLPHQVLRQIURPBORFDWLRQDVORFDWLRQLQFXEHVDOHV GHILQHGLPHQVLRQWRBORFDWLRQDVORFDWLRQLQFXEHVDOHV

Data Mining Lecture 2 23

0HDVXUHV7KUHH&DWHJRULHV

á GLVWULEXWLYHLIWKHUHVXOWGHULYHGE\DSSO\LQJWKH

IXQFWLRQWRQDJJUHJDWHYDOXHVLVWKHVDPHDVWKDW

GHULYHGE\DSSO\LQJWKHIXQFWLRQRQDOOWKHGDWDZLWKRXW

SDUWLWLRQLQJ

á (JFRXQW VXP PLQ PD[ 

á DOJHEUDLFLILWFDQEHFRPSXWHGE\DQDOJHEUDLFIXQFWLRQ

ZLWK 0 DUJXPHQWV ZKHUH0LVDERXQGHGLQWHJHU HDFK

RIZKLFKLVREWDLQHGE\DSSO\LQJDGLVWULEXWLYHDJJUHJDWH

IXQFWLRQ

á (JDYJ PLQB1 VWDQGDUGBGHYLDWLRQ 

á KROLVWLFLIWKHUHLVQRFRQVWDQWERXQGRQWKHVWRUDJHVL]H

QHHGHGWRGHVFULEHDVXEDJJUHJDWH

á (JPHGLDQ PRGH UDQN  Data Mining Lecture 2^24

$&RQFHSW+LHUDUFK\'LPHQVLRQ ORFDWLRQ

all

Europe North_America

Germany Spain Canada Mexico

Vancouver

L. Chan M. Wind

all

region

office

country

city Frankfurt Toronto

Data Mining Lecture 2 25

0XOWLGLPHQVLRQDO'DWD

á 6DOHVYROXPHDVDIXQFWLRQRISURGXFW PRQWKDQGUHJLRQ

Product

Region

Month

Dimensions: Product, Location, Time Hierarchical summarization paths

Industry Region Year

Category Country Quarter

Product City Month Week

Office Day

Data Mining Lecture 2 26

$6DPSOH'DWD&XEH

Total annual sales

Date of TV in U.S.A.

Product

Country

sum
sum
TV
VCR
PC

1Qtr 2Qtr (^) 3Qtr 4Qtr U.S.A

Canada

Mexico

sum

Data Mining Lecture 2 27

&XERLGV&RUUHVSRQGLQJWRWKH&XEH

all

product (^) date country

product,date product,country date, country

product, date, country

0-D(apex) cuboid

1-D cuboids

2-D cuboids

3-D(base) cuboid

Data Mining Lecture 2 28

%URZVLQJD'DWD&XEH

á 9LVXDOL]DWLRQ

á 2/$3FDSDELOLWLHV

á ,QWHUDFWLYHPDQLSXODWLRQ

Data Mining Lecture 2 29

7\SLFDO2/$32SHUDWLRQV

á 5ROOXS GULOOXS VXPPDUL]HGDWD ≤ E\FOLPELQJXSKLHUDUFK\RUE\GLPHQVLRQUHGXFWLRQ á 'ULOOGRZQ UROOGRZQ UHYHUVHRIUROOXS ≤ IURPKLJKHUOHYHOVXPPDU\WRORZHUOHYHOVXPPDU\RUGHWDLOHG GDWDRULQWURGXFLQJQHZGLPHQVLRQV á 6OLFHDQGGLFH ≤ SURMHFWDQGVHOHFW á 3LYRW URWDWH  ≤ UHRULHQWWKHFXEHYLVXDOL]DWLRQ'WRVHULHVRI'SODQHV á 2WKHURSHUDWLRQV ≤ GULOODFURVVLQYROYLQJ DFURVV PRUHWKDQRQHIDFWWDEOH ≤ GULOOWKURXJKWKURXJKWKHERWWRPOHYHORIWKHFXEHWRLWVEDFNHQG UHODWLRQDOWDEOHV XVLQJ64/

Data Mining Lecture 2 30

2YHUYLHZ

á :KDWLVDGDWDZDUHKRXVH"

á $PXOWLGLPHQVLRQDOGDWDPRGHO

á 'DWDZDUHKRXVHDUFKLWHFWXUH

á 'DWDZDUHKRXVHLPSOHPHQWDWLRQ

á )XUWKHUGHYHORSPHQWRIGDWDFXEHWHFKQRORJ\

á )URPGDWDZDUHKRXVLQJWRGDWDPLQLQJ

Data Mining Lecture 2 37

(IILFLHQW'DWD&XEH&RPSXWDWLRQ

á 'DWDFXEHFDQEHYLHZHGDVDODWWLFHRIFXERLGV

≤ 7KHERWWRPPRVWFXERLGLVWKHEDVHFXERLG
≤ 7KHWRSPRVWFXERLG DSH[ FRQWDLQVRQO\RQHFHOO
≤ +RZPDQ\FXERLGVLQDQQGLPHQVLRQDOFXEHZLWK/OHYHOV"

á 0DWHULDOL]DWLRQRIGDWDFXEH

≤ 0DWHULDOL]HHYHU\ FXERLG  IXOOPDWHULDOL]DWLRQ QRQH QR
PDWHULDOL]DWLRQ RUVRPH SDUWLDOPDWHULDOL]DWLRQ
≤ 6HOHFWLRQRIZKLFKFXERLGVWRPDWHULDOL]H

á %DVHGRQVL]HVKDULQJDFFHVVIUHTXHQF\HWF

∏(^ +

n i i

T L

Data Mining Lecture 2 38

&XEH2SHUDWLRQ

á &XEHGHILQLWLRQDQGFRPSXWDWLRQLQ'04/ GHILQHFXEHVDOHV>LWHPFLW\\HDU@VXP VDOHVBLQBGROODUV FRPSXWHFXEHVDOHV á 7UDQVIRUP LW LQWR D 64/OLNH ODQJXDJH ZLWK D QHZ RSHUDWRU FXEHE\LQWURGXFHGE\UD\HWDO∑ 6(/(&7LWHPFLW\\HDU680 DPRXQW )5206$/( &8%(%<LWHPFLW\\HDU á 1HHGFRPSXWHWKHIROORZLQJURXS%\V GDWHSURGXFWFXVWRPHU  GDWHSURGXFW  GDWHFXVWRPHU  SURGXFWFXVWRPHU  GDWH  SURGXFW  FXVWRPHU

(city) (item)

(year)

(city, item) (city, year) (item, year)

(city, item, year)

Data Mining Lecture 2 39

&XEH&RPSXWDWLRQ52/$3%DVHG0HWKRG

á (IILFLHQWFXEHFRPSXWDWLRQPHWKRGV

≤ 52/$3EDVHGFXELQJDOJRULWKPV $JDUZDOHWDO∑
≤ $UUD\EDVHGFXELQJDOJRULWKP =KDRHWDO∑
≤ %RWWRPXSFRPSXWDWLRQPHWKRG %H\HU 5DPDUNULVKQDQ∑

á 52/$3EDVHGFXELQJDOJRULWKPV

≤ 6RUWLQJKDVKLQJDQGJURXSLQJRSHUDWLRQVDUHDSSOLHGWRWKH
GLPHQVLRQDWWULEXWHVLQRUGHUWRUHRUGHUDQGFOXVWHUUHODWHG
WXSOHV
≤ *URXSLQJLVSHUIRUPHGRQVRPHVXEDJJUHJDWHVDVD¥SDUWLDO

JURXSLQJVWHSμ ≤ $JJUHJDWHVPD\EHFRPSXWHGIURPSUHYLRXVO\FRPSXWHG DJJUHJDWHVUDWKHUWKDQIURPWKHEDVHIDFWWDEOH

Data Mining Lecture 2 40

,QGH[LQJ2/$3'DWD%LWPDS,QGH[

á ,QGH[RQDSDUWLFXODUFROXPQ á (DFKYDOXHLQWKHFROXPQKDVDELWYHFWRUELWRSLVIDVW á 7KHOHQJWKRIWKHELWYHFWRURIUHFRUGVLQWKHEDVHWDEOH á 7KHLWKELWLVVHWLIWKHLWKURZRIWKHEDVHWDEOHKDVWKH YDOXHIRUWKHLQGH[HGFROXPQ á QRWVXLWDEOHIRUKLJKFDUGLQDOLW\GRPDLQV

C u st R eg io n T yp e C 1 A s ia R e ta il C 2 E u ro p e D e a le r C 3 A s ia D e a le r C 4 A m e ric a R e ta il C 5 E u ro p e D e a le r

R ecID R etail D ealer 1 1 0 2 0 1 3 0 1 4 1 0 5 0 1

R ecID Asia E u ro p e Am erica 1 1 0 0 2 0 1 0 3 1 0 0 4 0 1 0 5 0 0 1

Base table Index on Region Index on Type

Data Mining Lecture 2 41

,QGH[LQJ2/$3'DWD-RLQ,QGLFHV

á -RLQLQGH[-, 5LG6LG ZKHUH 5 5LG´ 6 6LG´ á 7UDGLWLRQDOLQGLFHVPDSWKHYDOXHVWRDOLVW RIUHFRUGLGV ≤ ,WPDWHULDOL]HVUHODWLRQDOMRLQLQ-,ILOHDQG VSHHGVXSUHODWLRQDOMRLQ≥DUDWKHUFRVWO
RSHUDWLRQ á ,QGDWDZDUHKRXVHVMRLQLQGH[UHODWHVWKH YDOXHVRIWKHGLPHQVLRQVRIDVWDUWVFKHPD WRURZVLQWKHIDFWWDEOH ≤ (JIDFWWDEOH6DOHVDQGWZRGLPHQVLRQV FLW\DQGSURGXFW á $MRLQLQGH[RQFLW\PDLQWDLQVIRUHDFK GLVWLQFWFLW\DOLVWRI5,'VRIWKH WXSOHVUHFRUGLQJWKH6DOHVLQWKHFLW
≤ -RLQLQGLFHVFDQVSDQPXOWLSOHGLPHQVLRQV

Data Mining Lecture 2 42

(IILFLHQW3URFHVVLQJ2/$34XHULHV

á 'HWHUPLQHZKLFKRSHUDWLRQVVKRXOGEHSHUIRUPHGRQWKH

DYDLODEOHFXERLGV

≤ WUDQVIRUPGULOOUROOHWFLQWRFRUUHVSRQGLQJ64/DQGRU2/$
RSHUDWLRQVHJGLFH VHOHFWLRQSURMHFWLRQ

á 'HWHUPLQHWRZKLFKPDWHULDOL]HGFXERLG V WKHUHOHYDQW

RSHUDWLRQVVKRXOGEHDSSOLHG

á ([SORULQJLQGH[LQJVWUXFWXUHVDQGFRPSUHVVHGYVGHQVH

DUUD\VWUXFWXUHVLQ02/$

Data Mining Lecture 2 43

0HWDGDWD5HSRVLWRU\

á 0HWDGDWDLVWKHGDWDGHILQLQJZDUHKRXVHREMHFWV,WKDVWKH IROORZLQJNLQGV ≤ 'HVFULSWLRQRIWKHVWUXFWXUHRIWKHZDUHKRXVH á VFKHPDYLHZGLPHQVLRQVKLHUDUFKLHVGHULYHGGDWD GHIQGDWDPDUW ORFDWLRQVDQGFRQWHQWV ≤ 2SHUDWLRQDOPHWDGDWD á GDWDOLQHDJH KLVWRU\RIPLJUDWHGGDWDDQGWUDQVIRUPDWLRQSDWK FXUUHQF
RIGDWD DFWLYHDUFKLYHGRUSXUJHG PRQLWRULQJLQIRUPDWLRQ ZDUHKRXVH XVDJHVWDWLVWLFVHUURUUHSRUWVDXGLWWUDLOV ≤ 7KHDOJRULWKPVXVHGIRUVXPPDUL]DWLRQ ≤ 7KHPDSSLQJIURPRSHUDWLRQDOHQYLURQPHQWWRWKHGDWDZDUHKRXVH ≤ 'DWDUHODWHGWRV\VWHPSHUIRUPDQFH á ZDUHKRXVHVFKHPDYLHZDQGGHULYHGGDWDGHILQLWLRQV ≤ %XVLQHVVGDWD á EXVLQHVVWHUPVDQGGHILQLWLRQVRZQHUVKLSRIGDWDFKDUJLQJSROLFLHV

Data Mining Lecture 2 44

'DWD:DUHKRXVH%DFN(QG7RROVDQG8WLOLWLHV

á 'DWDH[WUDFWLRQ ≤ JHWGDWDIURPPXOWLSOHKHWHURJHQHRXVDQGH[WHUQDOVRXUFHV á 'DWDFOHDQLQJ ≤ GHWHFWHUURUVLQWKHGDWDDQGUHFWLI\WKHPZKHQSRVVLEOH á 'DWDWUDQVIRUPDWLRQ ≤ FRQYHUWGDWDIURPOHJDF\RUKRVWIRUPDWWRZDUHKRXVHIRUPDW á /RDG ≤ VRUWVXPPDUL]HFRQVROLGDWHFRPSXWHYLHZVFKHFNLQWHJULW\ DQGEXLOGLQGLFLHVDQGSDUWLWLRQV á 5HIUHVK ≤ SURSDJDWHWKHXSGDWHVIURPWKHGDWDVRXUFHVWRWKH ZDUHKRXVH

Data Mining Lecture 2 45

2YHUYLHZ

á :KDWLVDGDWDZDUHKRXVH"

á $PXOWLGLPHQVLRQDOGDWDPRGHO

á 'DWDZDUHKRXVHDUFKLWHFWXUH

á 'DWDZDUHKRXVHLPSOHPHQWDWLRQ

á )XUWKHUGHYHORSPHQWRIGDWDFXEHWHFKQRORJ\

á )URPGDWDZDUHKRXVLQJWRGDWDPLQLQJ

Data Mining Lecture 2 46

'LVFRYHU\'ULYHQ([SORUDWLRQRI'DWD&XEHV

á +\SRWKHVLVGULYHQH[SORUDWLRQE\XVHUKXJHVHDUFKVSDFH á 'LVFRYHU\GULYHQ 6DUDZDJLHWDO∑ ≤ SUHFRPSXWHPHDVXUHVLQGLFDWLQJH[FHSWLRQVJXLGHXVHULQWKHGDWD DQDO\VLVDWDOOOHYHOVRIDJJUHJDWLRQ ≤ ([FHSWLRQVLJQLILFDQWO\GLIIHUHQWIURPWKHYDOXHDQWLFLSDWHGEDVHG RQDVWDWLVWLFDOPRGHO ≤ 9LVXDOFXHVVXFKDVEDFNJURXQGFRORUDUHXVHGWRUHIOHFWWKHGHJUHH RIH[FHSWLRQRIHDFKFHOO ≤ &RPSXWDWLRQRIH[FHSWLRQLQGLFDWRU PRGHOLQJILWWLQJDQGFRPSXWLQJ 6HOI([S,Q([SDQG3DWK([SYDOXHV FDQEHRYHUODSSHGZLWKFXEH FRQVWUXFWLRQ

Data Mining Lecture 2 47

([DPSOHV'LVFRYHU\'ULYHQ'DWD&XEHV

Data Mining Lecture 2 48

&RPSOH[$JJUHJDWLRQDW0XOWLSOH*UDQXODULWLHV

0XOWL)HDWXUH&XEHV

á 0XOWLIHDWXUHFXEHV 5RVVHWDO &RPSXWHFRPSOH[TXHULHV LQYROYLQJPXOWLSOHGHSHQGHQWDJJUHJDWHVDWPXOWLSOHJUDQXODULWLHV á ([*URXSLQJE\DOOVXEVHWVRI^LWHPUHJLRQPRQWK`ILQGWKH PD[LPXPSULFHLQIRUHDFKJURXSDQGWKHWRWDOVDOHVDPRQJ DOOPD[LPXPSULFHWXSOHV VHOHFWLWHPUHJLRQPRQWKPD[ SULFH VXP 5VDOHV IURPSXUFKDVHV ZKHUH\HDU  FXEHE\LWHPUHJLRQPRQWK VXFKWKDW5SULFH PD[ SULFH á &RQWLQXLQJWKHODVWH[DPSOHDPRQJWKHPD[SULFHWXSOHVILQGWKH PLQDQGPD[VKHOIOLYHDQGILQGWKHIUDFWLRQRIWKHWRWDOVDOHV GXHWRWXSOHWKDWKDYHPLQVKHOIOLIHZLWKLQWKHVHWRIDOOPD[ SULFHWXSOHV