A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining

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1 A Mehodology fo Deecng he Change of Cusome Behavo based on Assocaon Rule Mnng Hee Seo Song, Soung He Km KAIST Gaduae School of Managemen Jae Kyeong Km KyungHee Unvesy Absac Undesandng and adapng o changes of cusome behavo s an mpoan aspec fo a company o suvve n connuously changng envonmen. The am of hs pape s o develop a mehodology whch deecs changes of cusome behavo auomacally fom cusome pofles and sales daa a dffeen me snapshos. Fo hs pupose, we fs defne hee ypes of changes as emegng paen, unexpeced change and he added / peshed ule. Then, we develop smlay and dffeence measues fo ule machng o deec all ypes of change. Fnally, he degee of change s evaluaed o deec sgnfcanly changed ules. Ou poposed mehodology can evaluae degee of changes as well as deec all nds of change auomacally fom dffeen me snapsho daa. A case sudy fo evaluaon and paccal busness mplcaons fo hs mehodology ae also povded. Keywods : Daa Mnng, Assocaon Rule Mnng, Change Mnng 1. Inoducon Undesandng and adapng o changes of cusome behavo s an mpoan aspec of suvvng n a connuously changng envonmen. Especally fo busnesses, nowng wha s changng and how has been changed s of cucal mpoance because allows busnesses o povde he gh poducs and sevces o su he changng mae needs (Lu e al Daa mnng s he pocess of exploaon and analyss of lage quanes of daa n ode o dscove meanngful paens and ules. Bu much of exsng daa mnng eseach has been focused on devsng echnques o buld accuae models and o dscove ules. Relavely lle aenon has been pad o mnng changes n daabases colleced ove me (Lu e al In hs pape, we develop a mehodology whch deecs changes auomacally fom cusome pofles and sales daa a dffeen peods of me. The mos common appoach o dscove changes beween wo daases s o geneae ules fom each daase and decly compae he ules by ule machng. Bu hs s no a smple pocess because of he followng easons. Fs, some ules canno be easly compaed due o dffeen 871

2 ule sucues. Second, even wh mached ules, s dffcul o now wha nd of change and how much change has occued. To smplfy hese dffcules, we fs defne hee ypes of changes as emegng paen, unexpeced change and he added / peshed ule. Then we develop smlay and dffeence measues fo ule machng o deec all ypes of change. Fnally, he degee of change s evaluaed o deec sgnfcanly changed ules. The poposed mehodology can evaluae degee of changes as well as deec all nds of changes auomacally fom dffeen me snapsho daa. Deeced changes can be usefully appled o plan vaous nche maeng campagns. Fo example, f a manage can fnd ou ha a cean cusome s pefeence has moved fom a medum-sze ca o a lage-sze ca, hen ha manage can esablsh a ade-n plan fo cusomes who have a medum-sze ca and have he nenon of buyng a lage-sze ca fo eplacemen. Assocaon ule mnng fnds neesng assocaon elaonshps among a lage se of daa ems (Agawal e al Wh massve amouns of daa connuously beng colleced and soed, many nduses ae becomng neesed n mnng assocaon ules fom he daabases. Assocaon ule mnng s used as a basc mnng mehodology n ou eseach. 2. Bacgound 2.1 Assocaon Rule Mnng A ypcal assocaon ule has an mplcaon of he fom A B whee A s an emse and B s an emse ha conans only a sngle aomc condon. The suppo of an assocaon ule s he pecenage of ecods conanng emses A and B ogehe. The confdence of a ule s he pecenage of ecods conanng emse A ha also conan emse B. Suppo epesens he usefulness of dscoveed ule and he confdence epesens ceany of he deeced assocaon ule. Fgue 1 shows wo assocaon ules of whch suppo s he same bu he confdence of Rule 2 s lage han ha of Rule 1. Recod ID [Fgue 1] Daase and dscoveed assocaon ules Iems Bough 2000 A, B, C 1000 A, C 4000 A, D 5000 B, E, F Dscoveed Assocaon Rules Rule 1 : A C (suppo: 50 %, confdence: 66.6 % Rule 2 : C A (suppo: 50 %, confdence: 100 % Assocaon ule mnng fnds all collecons of ems n a daabase whose confdence and suppo mee o exceed pe-specfed heshold value. Apo algohm s one of he pevalen echnques used o fnd assocaon ules (Agawal e al Apo opeaes n wo phases. In he fs phase, all lage emses ae geneaed. Ths phase ulzes he 872

3 downwad closue popey of suppo. The second phase of he algohm geneaes ules fom he se of all lage emses. Please efe o he sudy of Agawal e al. (1993 fo a moe deal. 2.2 Daa mnng n a changng envonmen Thee ae exsng wos ha have been done on leanng and mnng (Bay and Pazzan 1999; Gan e al. 1999; Han and Kambe 2001; Lu e al n a changng envonmen. All he followng elaed wos focus on dynamc aspecs o compason beween wo dffeen daases o ules. The fs eseach end elaed o ou wo s o dscove Emegng Paens (Agawal and Psala 1995; Dong and L 1999; L e al The eseach es o fnd Emegng Paens (EPs whch ae defned as emses whose suppos ncease sgnfcanly fom one daase o anohe. EPs can capue emegng ends n mesamped daabases, o useful conass beween daa classes. Bu hey do no consde he sucual changes n he ules. Fo example, n a mae base, hese echnques can dscove sgnfcan ule changes whch ncease gowh / decease ae of consumpon ove me bu canno deec any unexpeced changes such as a change fom coffee ea o coffee ml. The second eseach feld s mnng class compasons o dscmnae beween dffeen classes (Bay and Pazzan 1999; Gan e al. 1999; Han and Kambe Gan e al. (1999 pesens a geneal famewo fo measung changes n wo models. Essenally, he dffeence beween wo models s quanfed as he amoun of wo equed o ansfom one model no he ohe. I povdes devaons measue beween wo mnng model o focused egons bu canno be decly appled o deec cusome behavo changes because does no povde whch aspecs ae changed and wha nd of changes have occued. Bay and Pazzan (1999 and Han and Kambe (2001 also povde echnques fo undesandng he dffeences beween seveal conasng goups. Bu hese echnques can only deec change abou he same sucued ule. Fnally, Lu e al. (2000 pesens a echnque fo change mnng by ovelappng wo decson ees whch ae geneaed fom dffeen me snapshos. Bu he change mnng echnque usng decson ees canno deec complee ses of change. Snce decson ee echnques un whn a specfed obecve class, only changes abou ha desgnaed consequen abue can be deeced. Ths appoach can be used only n cases whch have a specfc eseach queson. Also, hs echnque does no povde any nfomaon fo he ype of change and he degee of change. Besdes of he above sudes, he eseaches fo ule manenance and me sees also handle dynamc suaons bu we om he explanaons because of page lmaon. 3. Poblem In hs secon, we examne all possble ypes of change based on pas eseach and busness 873

4 equemens (Dong and L 1999; Lanqullon 1999; Lu and Hsu 1996; Lu e al. 1997; Padmanabhan and Tuzhln 1999; Suzu Afe ha, each ype of change and change deecon poblem ae defned. Le s defne he followng noaon. + D, D : daases a me, + + R, R : dscoveed assocaon uleses a me, , : each ule fom coespondng ulese R, R,whee = 1,2, L, R, = 1,2, L, R Sup ( : suppo of n me daase Dong and L (1999 noduced Emegng Paens concep whch capues sgnfcan changes and dffeences beween daases. Emegng paens ae defned as emses whose suppos ncease sgnfcanly fom one daase o anohe. We bng fom sudy of Dong and L (1999 he em emegng paen wh he followng modfed defnon fo ou eseach. Defnon 1] Emegng Paens Fo ule +, f he followng wo condons ae me, hen we call he ule of Emegng Paen wh espec o. + (1 Condonal and consequen pas ae he same beween, (2 Suppos of wo ules ae sgnfcanly dffeen Example 1] : Income = Hgh, Age = Hgh Model = Lage (Suppo = : Income = Hgh, Age = Hgh Model = Lage (Suppo = 0.13 In hs case, + s he emegng paen wh espec o f we specfy mnmum gowh ae o be 0.2. Ths s because he wo ules have same ule sucue and he gowh ae s 0.3. The ohe ype of change s unexpecedness whch s found fom many sudes abou dscoveng neesng paens (Lu and Hsu 1996; Lu e al. 1997; Padmanabhan and Tuzhln 1999; Slbeschaz and Tuzhln 1996; Suzu Lu and Hsu (1996 defned unexpeced changes as ule smlay and dffeence aspecs. They dsngushed unexpeced changes o unexpeced condon changes and unexpeced consequen changes based on a synacc compason beween a ule and a belef. Bu we only adap unexpeced consequen changes because mos unexpeced condon changes usually mae no sense. These unexpeced consequen changes ae he second ype of change o deec whch has a dffeen ule sucue ove me. Theefoe we edefne he em unexpeced changes le he followng fom he sudy of Lu and Hsu (1996. Defnon 2] Unexpeced Changes (o Unexpeced Consequen Changes + s unexpeced change wh espec o f he condonal pas of smla, bu he consequen pas of he wo ules ae que dffeen. +, ae Example 2] : Income = Hgh, Age = Hgh Model = Lage + : Income = Hgh, Age = Hgh Model = Medum 874

5 In hs case, + condonal pas of dffeen. s unexpeced consequen change wh espec o snce he +, ae smla, bu he consequen pas of he wo ules ae que Ohe ypes of change ae added ules and peshed ules (Lanqullon An added ule s a newly asen ule whch could no be found n he pas and a peshed ule s a dsappeaed ule whch can be found only n he pas bu no he pesen. We defne added and peshed ule as follows. Defnon 3] Added ules / Peshed ules + s an added ule f all he condons and consequens ae que dffeen fom any of n R and s a peshed ule f all he condons and consequens ae que dffeen fom any of + n R +. We used he ems smla and que dffeen n he above defnons. Those ems ae used o compae wo ules n synacc aspecs and o udge degee of smlay and dffeence. Bu he ems smla and que dffeen ae que subecve and dffeen fom each ndvdual. Theefoe we defne Rule Machng Theshold (RMT whch can be dffeenly decded by ndvdual use. Fgue 2 explans he concep of RMT and povdes how he dffeen ypes of change can be dsngushed by RMT. Compleely Dffeen [Fgue 2] Dffeen ypes of change n synacc aspecs RMT Compleely Same Added/Peshed Rule Unexpeced Change Emegng Paen Fnally, we defne he degee of change as he measue of how much change has occued. The degee of change has o be evaluaed dffeenly by each ype of change because of dffeen chaacescs. The man way of evaluang degee of change wll be explaned n he nex secon. Now, he change deecon poblem s defned as follows usng he above defnons of each change ype. Defnon 4] Change deecon poblem The change deecon poblem consss of fndng all emegng paens, unexpeced changes and added/peshed ules beween daases whch ae colleced fom dffeen peods and anng he changed ules n each ype by he degee of change. 4. Mehodology 4.1 Oveall pocedue 875

6 Now we sugges he mehodology fo he change deecon poblem. The mehodology consss of he followng hee phases n Fgue 3. Phase I [Fgue 3] Oveall pocedue o deec change Geneaon of Assocaon Rules usng APRIORI Algohm + Inpu : D, D, Suppo / Confdence Theshold + Oupu : R, R Phase II Dscovey of Changed Rule usng Rule Machng Mehod + Inpu : R, R, RMT(Rule Machng Theshold Oupu : Changed ulese fo each ype of change Phase III Evaluang Degee of Change Inpu : Changed ulese Oupu : Sgnfcanly changed ulese In phase I, wo assocaon uleses ae geneaed fom each daase by usng Apo algohm (Agawal e al In phase II, he changed ulese s geneaed by usng he ule machng mehod whch compaes wo ules seleced fom each ulese. We adaped he ule machng mehod developed by Lu and Hsu (1996 and modfed o dsngush beween he above hee ypes of change. Fo effcen ule machng, smlay and dffeence measues ae developed. Ou ule machng mehod can deec all ypes of changed ules ncludng emegng paens, unexpeced changes, added and peshed ules. In phase III, vaous changed ules deeced n phase II ae aned accodng o he pedefned degee of change whch s a measue o evaluae how much change has occued. 4.2 Dscovey of assocaon ule Fo he Apo algohm, we have o pefom he dscezaon pocess (Hussan e al o dscove assocaon ules. In hs pape, all he values n he daase ae assumed o be dscezed fo he smplcy of explanaon. We need wo daases colleced a dffeen mes, mnmum suppo levels and mnmum confdence levels as npus. In ou expeence, a lowe mnmum suppo level s pefeed o dscove assocaon ules. If he mnmum suppo level s se vey hgh, we may lose he oppouny o deec he emegng paens whch have lage gowh (o decease aes bu ae ae ems. 4.3 Dscovey of changed ule In hs phase, vaous ypes of changed ules ae deeced usng he ule machng mehod. The npu of phase II s dscoveed uleses a me and +, and he Rule Machng Theshold (RMT whch s specfed by he use. Phase II s composed of he followng hee seps. 876

7 Sep [1] Calculae he maxmum smlay value fo each ule n me and +. + Sep [2] Fo each ule, calculae he dffeence measues beween,. Sep [3] Classfy he ype of change fo he ules usng he maxmum smlay value and he dffeence measues. Fo he explanaon of each sep, some noaons ae befly defned. δ : Dffeence measue. Degee of dffeence beween and + ( 1 δ 1, 0 δ 1 s : Smlay measue. Degee of smlay beween and + ( 0 s 1 + l : Degee of abue mach of he condonal pas l = A / max( X, X c : Degee of abue mach of he consequen pas A : Numbe of abues common o boh condonal pas of X : Numbe of abues n he condonal pas of X + : Numbe of abues n he condonal pas of + x : Degee of value mach of he h machng abue n A y : Degee of value mach of he consequen abue c 1, = 0, f same consequen ohewse abue x 1, = 0, f same value ohewse y and 1, = 0, + f same value ohewse Now we povde smlay measue as follows, adaped fom he sudy of Lu and Hsu (1996. s l = 0 A x A c y, f, f A A 0 = 0 In s, l x / A epesens a smlay of condonal pa, and c y epesens a A smlay of consequen pa beween and +. If he condonal and consequen pas beween and + ae he same, hen he degee of smlay becomes 1. The smlay measue can ae any value beween 0 and 1. To deec added and peshed ules, he maxmum smlay value s povded as follows. s s = max( s1, s2,, s R ; + = max( s1, s2,, s ; R Maxmum Smlay Value Maxmum Smlay Value of of + The maxmum smlay value ndcaes whehe he ule s added o peshed. If s < RMT, hen s ecognzed as a peshed ule. If s < RMT, hen he ule + becomes an added ule. Example 3] Assume he followng ules ae geneaed fom each daase 1 : Income = Hgh Sales = Hgh 2 : Age = Hgh, Pefeence = Pce Sales = Hgh + 1 : Income = Hgh Sales = Hgh + 2 : Age = Hgh Sales = Hgh D and D

8 + 3 : Income = Hgh, Pefeence = Pce Sales = Low We can compue he smlay measue beween 2, + 2 and he maxmum smlay value of 2 as follows s 22 = = 0.5, s2 = max(0,0.5,0 = In he same manne, we can compue he maxmum smlay value of each ule. s 1 = max( 1,0,0 = 1 s2 = max(0,0.5,0 = s 1 = max( 1,0 = 1 s2 = max(0,0.5 = 0.5 s3 = max(0,0 = 0 If we specfy RMT o be 0.4, hen we can conclude ha only + 3 s an added ule. As we can see fom example 3, he maxmum smlay value n sep [1] s used o dscove added ules o peshed ules. The pupose of sep [2] s o deec unexpeced changes and emegng paens. To deec unexpeced change, a dffeence measue s povded as follows. δ l = A y A x y, f, f A A 0, c = 0, c = 1 = 1 As defned above n he poblem defnon secon, f condonal pas ae smla bu consequen pas ae dffeen, hen hs ule s called as an unexpeced consequen. I means ha he smlay of he condonal pa s geae han ha of he consequen pa. Based on hs measue, we can udge whehe he ule + s an unexpeced consequen change wh espec o. In summay, f δ > 0, hen ule + s an unexpeced consequen change wh espec o. If δ < 0, hen ule + s an unexpeced condon change wh espec o. If δ = 0, hen wo ules and + ae he same ules o compleely dffeen ules. Theefoe addonal measues such as l, y, ec should be povded n case of δ = 0. If hese values ae 1 hen we can decly fnd ha wo ules ae same. We compue dffeence measues only n he case of c = 1. If abues of consequen pas beween he wo ules ae dffeen, maes no sense o compae he degee of dffeence because hese wo ules ae compleely dffeen ules. The sep [3] classfes he ules as hee ypes of change. To classfy he ype of change, addonal compuaon s needed. Fo example, alhough + s udged o be an unexpeced change wh egad o by he dffeence measue, we canno conclude decly whehe s an unexpeced change o no. Because + can be an emegng paen wh egad o m whch has he same sucue wh +. In hs case, + should be classfed no an emegng paen and no o be classfed as an unexpeced change. As we canno conclude based on δ alone whehe + s an 878

9 unexpeced change o an emegng paen, we povde he followng modfed dffeence measue. ' 1, f max( s, s = 1 δ = δ, whee = 0, ohewse The fac ha s (o s s equal o 1 means ha he same ule exss n anohe ulese. Tha means + ' s lely o be classfed no an emegng paen. If δ s geae han he pespecfed RMT, hen he ule + s concluded o be an unexpeced change wh espec o. Example 4] 1 : Income = Hgh, Pefeence = Pce Sales = Low 2 : Age = Hgh, Pefeence = Pce Sales = Hgh + 1 : Income = Hgh Sales = Hgh + 2 : Age = Hgh Sales = Hgh + 3 : Income = Hgh, Pefeence = Pce Sales = Low Wh he assocaon ulese, we can compue he dffeence and modfed dffeence measue beween 2 and + 3 as follows. ' δ 23 = 0.5 δ 23 = = 0.5 If we specfy ha RMT s equal o 0.4, we canno conclude ha 3 + s an unexpeced consequen change wh espec o 2 because 3 + has a same ule sucue wh 1. Theefoe, we can conclude ha 3 + s an emegng paen of 1. And 3 + s no hough o be an unexpeced consequen change wh espec o 1. Table 1 summazes he value of each measue fo each ype of change. [Table 1] Value of measue fo each ype of change Type of Change Value of measue o classfy Emegng Paen δ 0, ( x > 0 o y > 0 o l > 0 Unexpeced Consequen δ = > 0, δ A ' RMT Added Rule (Peshed Rule s < RMT ( s < RMT 4.4 Evaluang he degee of change All he changed ules have o be aned by he degee of change. We wll explan he dea of evaluang he degee of change fo each ype of change. Fs, le s consde he unexpeced change. The followng example pesens why addonal measues should be equed. : Income = Hgh, Age = Hgh Model = Lage + : Pefeence = Pce, Age = Hgh Model = Small 879

10 If RMT value s se equal o 0.4, hen he ule + becomes an unexpeced consequen change wh espec o as δ = Bu hee exs wo poblems o conclude whehe hs change s sgnfcan. Fs, we canno capue hs change easly because condonal pas ae no same. Second, alhough we can undesand hs change, we do no now how much change has occued. Theefoe addonal logcal udgemen s equed o conclude whehe he degee of change s sgnfcan o no. Fo hs pupose, we adap he unexpecedness concep fom he sudy of Padmanabhan and Tuzhln (1999. They defne unexpecedness usng he excepon ule concep (Hussan e al. 2000; Suzu 1997 as follows. Defnon 5] Unexpecedness If an assocaon ule A B s unexpeced wh espec o he belef X Y, hen he followng mus hold. (1 B And Y = False (2 The ule X, A B holds. A new measue fo he degee of change of unexpeced consequen change s defned usng defnon 5. To measue he degee of unexpeced consequen change, s assumed o be a belef o exsng nowledge. Evey unexpeced consequen change sasfes above (1 condon of defnon 5 because of defnon 2. Fuhemoe he suppo value of he conuncon ule should be evaluaed o chec whehe (2 of defnon 5 holds o no. Fo example, a conuncon ule of he above example s as follows. : Income = Hgh, Age = Hgh, Pefeence = Pce Model = Small If he above conuncon ule,, s sascally lage (.e. has lage suppo value, hen we can conclude ha + s an unexpeced consequen change wh espec o by condon (2 of defnon 5. Theefoe, he suppo value of he conuncon ule can be egaded as he degee of change fo unexpeced consequen change. Bu he wo condons of defnon 5 ae no suffcen. If he suppo value of he conuncon ule s elavely small by compason o he suppo value of +, hen we canno conclude ha + s a sgnfcan unexpeced consequen change wh espec o. Addonal condons whch should be ncluded s ha he suppo value of should be enough lage o epesen +. Theefoe, he degee of change fo unexpeced consequen change should be composed of he suppo value of + and. Fgue 4 llusaes he above suaon. In Fgue 4, as moe exceponal cases occu fo cean exsng belefs o ules, we consde ha moe unexpeced consequen changes have occued. Now we ae eady o povde he followng measue fo he degee of unexpeced consequen change. 880

11 α + Sup = Sup ( + ( [Fgue 4] Concep fo degee of change of unexpeced change Age = Hgh Income = Hgh Pefeence = Pce Income = Hgh Age = Hgh Pefeence = Pce [Small excepon cases] [Lage excepon cases] Cusomes who have endency o buy lage szed ca ( Exsng Belef Cusomes who bough small szed ca a peod + ( New Dscoveed Rule Excepon Cases : cusomes who expeced o buy lage szed ca bu bough small szed ca In he case of emegng paen, I s moe smple o evaluae he sgnfcance level han he case of unexpeced change. The gowh o decease ae ae used as he measue fo hs ype of change. To evaluae he degee of change fo added and peshed ule cases, he suppo value of hose ules and he maxmum smlay value ae used. As menoned befoe, he maxmum smlay value of he ule epesens he degee of smlay of he mos smla ule o he ohe ulese. If hee s a suaon ha he suppo values of wo added ules ae same, we naually place moe mpoance on he ule whch has less maxmum smlay value. Such a ule gves moe sgnfcance han he ohe ule. The measue of he degee of change, α, s summazed as follows. Based on he value of α, we can an he changed ules n each ype of change. α + Sup ( Sup ( Sup ( + Sup ( = + Sup ( (1 s Sup ( + (1 s Sup (, emegng paen case, une xpeced change case, peshed ule case, added ule case 5. Evaluaon Fo he evaluaon of poposed mehodology, he sysem s mplemened usng Vsual Basc 6.0. The case sudy has been conduced o evaluae how well he sysem pefoms s nended as of deecng sgnfcan changes. The daase s pepaed fom an Koean onlne shoppng mall whch sells vaous consume goods. The daase conans cusome pofles and puchasng hsoy such as age, ob, sex, addess, egsaon yea, cybe money, numbe of 881

12 puchases, oal puchase amoun, numbe of vss, paymen mehod dung one yea. We consuced a daa waehouse whch aggegaed hsocal daa by ndvdual cusome. We pepaed wo daase o deec sgnfcan changes of puchasng behavo by he cusomes. The fs daase conans pofles and puchasng hsoy nfomaon of cean cusomes who had bough moe han one cosmecs fom Feb/1/2000 o Jun/30/2000. The second daase conans he same nfomaon bu ncludes cusomes who had made one addonal puchase of cosmecs fom Jul/1/2000 o Jan/5/2001. Afe pepocessng he daa fo cleansng and dscezaon, an Apo echnque was appled o dscove he assocaon ules fom each daase. We seleced he numbe of puchases and oal sales amoun as oupu vaables. In he condon of 1 % mnmum suppo, 80 % mnmum confdence and maxmum emse of sze 3, he sysem found 127 assocaon ules fo he fs daase and 104 assocaon ules fo he second one. Gven a 0.4 Rule Machng Theshold (RMT, he sysem found 101 changed ules and 24 sgnfcanly changed ules. The numbe of changed ules fo each ype of change s povded n Table 2. [Table 2] Numbe of changed ules fo each ype of change Type of change Numbe of changed ules Numbe of sgnfcan changed ules Emegng Paens (Degee of change > 0.4 Unexpeced Changes 6 4 (Degee of change > 0.3 Added/Peshed Rules 3 3 (Degee of change > 0.01 Sgnfcan emegng paens, unexpeced changes, added/peshed ules ae summazed n Table 3, 4, and 5. [Table 3] Sgnfcan Emegng Paens (Degee of change > 0.4 (O + Rule Suppo + Sup Sup ( ( α ( > Vs=Low, Job=Specals OdCn=Low Vs=Low, ResevedMoney=Low OdCn=Low Vs=Low, ResevedMoney=Low Sales=Low Vs=Hgh, Job=Specals OdCn=Hgh Vs=Hgh, Job=Specals Sales=Hgh Vs=Hgh, Add=Daegu OdCn=Hgh Vs-Hgh, Add=Pusan OdCn=Hgh Vs=Hgh, Add=Pusan Sales=Hgh ResevedMoney=Low, Job=Suden Sales=Low Vs=Low, Add=ChungBu OdCn=Low

13 [Table 4] Sgnfcan Unexpeced Changes (Degee of change > ' δ δ α 1 Sex=F, Add=KyungNam OdCn=Low (Suppo : Regsday=Ths_yea, Add=KyungNam OdCn=Low (Suppo : Paymen=Cash, Add=KyungNam OdCn=Low (Suppo : ResevedMoney=Low, Add=KyungNam OdCn=Low (Suppo : Vs=Hgh, Add=KyungNam OdCn=Hgh (Suppo : Vs=Hgh, Add=KyungNam OdCn=Hgh (Suppo : Vs=Hgh, Add=KyungNam OdCn=Hgh (Suppo : Vs=Hgh, Add=KyungNam OdCn=Hgh (Suppo : [Table 5] Sgnfcan Added/Peshed Rules (Degee of change > 0.01 MSV Suppo α 1 Age=Teen Sales=Low Sex=F, Age=Teen Sales=Low Age=Thh, Add=Pusan Sales=Low Fom changed ules 4 and 5 n Table 3, we can see he apd gowh (90 % gowh n sales fo cusomes who ae specalss and vs he mall fequenly. Alhough he suppo value fo hem s low (0.021, 0.04, hose cusomes have he possbly o become loyal cusomes n he nea fuue because of hgh gowh ae. Theefoe, a maeng campagn o nvoe he evsng by hose cusomes should be developed. We can also denfy above ends n changed ule 1 of Table 3. Fom he changed ule 6, 7 and 8 n Table 3, we can see he apd gowh n sales fo cusomes who lve n Daegu, Pusan cy and vs he mall fequenly. Whou he change deecon mehodology, he maeng manage may undesand ha cusomes who lve n Daegu, Pusan cy and vs he mall fequenly ae no mpoan because of he low suppo value. Wh egad o unexpeced changes, we denfed 4 sgnfcan changes. Fom he changed ule 1 of Table 4, we can fnd ha sales fo female cusomes who lve n KyungNam ae low fom he fs daase. Bu n he second daase, we can see ha sales fo female cusomes who vs he mall fequenly ae hgh even f hey ae female cusomes who lve n KyungNam. I means ha he mpoance of cusomes who lve n KyungNam and vs he mall fequenly s gadually nceasng. Theefoe, a modfcaon fo he exsng maeng saegy and plan s equed. Changed ules 2, 3 and 4 n Table 4 can be nepeed smlaly. Fnally, we hee peshed ules ae found n Table 5. Fom Feb. o Jun. n 2000, mos of he cusomes wee aged wenh and sales fo he ohe cusomes wee vey low. Bu nowadays, we can fnd a end ha he age of he cusomes coves a wde ange. Theefoe addonal sevces and poducs fo eldes and eenages should be also developed. 883

14 6. Concluson In hs pape, we developed a mehodology whch deecs changes of cusome behavo auomacally fom cusome pofles and sales daa a dffeen me snapshos. The paccal applcaons and oppounes o use fo he mehodology ae as follows. Fs, n maco aspecs, busness manges can follow he changng ends usng change deecon mehodology. They need o analyze he cusome s changng behavos n ode o povde poducs and sevces ha su he changng needs of he cusomes. Second, n mco aspecs, can be possble fo a busness manage o undesand cusome needs moe deeply and desgn addonal nche maeng campagns usng hs mehodology. Knowng he hsoy of cusome behavo can gve a bee undesandng of cusome behavo. Some lmaons of suggesed mehodology can be descbed as follows. Wh egad o he numbe of age daases whch should be compaed, he mehodology s suggesed o only wo daases. If hee ae hee o moe daases o be compaed ove me, hen anohe mehodology wll have o be developed. The mehodology s un on he daases whch have descezed values. If hee s a daase whch has connuous values, hen a pe-pocessng sep fo dscezaon s needed. Vaous echnques fo dscezaon ae summazed n he sudy of Hussan e al. (1999. The ules fo he mehodology s come fom assocaon ule mnng. We do no consde ules geneaed fom anohe ule nducon mehod such as a decson ee. Bu hese assumpons ae easly loosened f we pepae funcons fo pocessng connuous vaables. Fnally, moe sophscaed evaluaon s needed. Bu we have plan o evaluae poposed measues and es sgnfcance of dscoveed change ule usng sascal mehod. As a fuhe eseach aea, we plan o exend ou mehodology o dscove changes of a moe geneal naue han assocaon ules. I wll be also pomsng o seup he campagn managemen plannng based on ou suggesed mehodology. And wll be also neesng o chec he effecveness of he campagn. We have also plan o apply ou mehodology o deec he changes of ndvdual level. We beleve ha he change deecon poblem wll become moe and moe mpoan as moe daa mnng applcaons ae mplemened. Refeences Agawal, R., Imelns, T., and Swam, A. Mnng assocaon ules beween ses of ems n lage daabases, Poceedngs of he ACM SIGMOD Confeence on Managemen of Daa, Agawal, R., and Psala, G. Acve daa mnng, Poceedngs of he Fs Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-95, Bay, S. D., and Pazzan, M. J. Deecng Change n Caegocal Daa: Mnng Conas Ses, Poceedngs of he Ffh Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-99,

15 Dong, G., and L, J. Effcen Mnng of Emegng Paens : Dscoveng Tends and Dffeences, Poceedngs of he Ffh Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-99, Gan, V., Gehe, J., and Ramashnan, R. A famewo fo measung changes n daa chaacescs, Poceedngs of he Egheenh ACM SIGACT-SIGMOD-SIGART Symposum on Pncples of Daabase Sysems (PODS-99, Han, J., and Kambe, M. Daa Mnng : Conceps and Technques, Mogan Kaufmann Publshes, San Fancsco, 2001, pp Hussan, F., Lu, H., Suzu, E., and Lu, H. Excepon Rule Mnng wh a Relave Ineesngness Measue, Poceedngs of Pacfc Asa Confeence on Knowledge Dscovey n Daabases (PAKDD, 2000, pp Hussan, F., Lu, H., Tan, C. L., and Dash, M. Dscezaon: An Enablng Technque, The Naonal Unvesy of Sngapoe Techncal Repo TRC6/99, June Lanqullon, C. Infomaon fleng n changng domans, Poceedngs of he Inenaonal Jon Confeence on Afcal Inellgence (IJCAI99, 1999, pp L, J., Dong, G., and Ramamohanaao, K. Mang Use of he Mos Expessve Jumpng Emegng Paens fo Classfcaon, Poceedngs of Pacfc Asa Confeence on Knowledge Dscovey n Daabases (PAKDD, Lu, B., and Hsu, W. Pos-Analyss of Leaned Rules, Poceedngs of he Theenh Naonal Confeence on Afcal Inellgence (AAAI-96, Lu, B., Hsu, W., and Chen, S. Usng geneal mpessons o analyze dscoveed classfcaon ules, Poceedngs of he Thd Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-97, Lu, B., Hsu, W., Han, H. S., and Xa, Y. Mnng Changes fo Real-Lfe Applcaons, n Publshng n he Second Inenaonal Confeence on Daa Waehousng and Knowledge Dscovey (DaWaK 2000, Y. Kambayash, M.K. Mohana, A.M. Toa (eds., Nahaezadeh, G., Taylo, C., and Lanqullon, C. Evaluang usefulness of dynamc classfcaon, Poceedngs of he Fouh Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-98, Padmanabhan, B., and Tuzhln, A. Unexpecedness as a measue of neesngness n nowledge dscovey, Decson Suppo Sysems (27, 1999, pp Slbeschaz, A., and Tuzhln, A. Wha maes paens neesng n nowledge dscovey sysems, IEEE Tans. On Knowledge and Daa Engneeng (8:6, 1996, pp Suzu, E. Auonomous dscovey of elable excepon ules, Poceedngs of he Thd Inenaonal Confeence on Knowledge Dscovey and Daa Mnng (KDD-97,