Fuzzy Clustering for TV Program Classification

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1 Fuzzy Clusterng for TV rogrm Clssfcton Yu Zhwen Northwestern olytechncl Unversty X n,.r.chn, 7007 Gu Jnhu Northwestern olytechncl Unversty X n,.r.chn, 7007 Zhou Xngshe Northwestern olytechncl Unversty X n,.r.chn, 7007 Yng Zhy Northwestern olytechncl Unversty X n,.r.chn, 7007 Astrct * In order to cheve TV progrm group recommendton, n pproch sed on fuzzy clusterng s proposed for progrm clssfcton n ths pper. Ths pper frstly descres the XML sed progrm descrpton metdt representton, n whch oth textul nd symolc nformton s ncluded. Secondly t presents the progrm feture extrcton nd presentton method. A progrm s defned s two vectors, one s sed on term sttstcs mplyng wht the progrm s out, nd the other reflects rodcstng chrcterstcs of the progrm. Then the clssfyng pproch sed on fuzzy clusterng s proposed. The pproch goes: normlzng orgnl dt, uldng fuzzy smlrty mtrx, nd then clusterng. The fnl fuzzy smlrty mtrx s constructed y comnng two fuzzy smlrty mtrces clculted from two dfferent spects.. Introducton Wth the rpd growth of DTV (Dgtl Televson) technologes, there exsts n overundnce of progrms vlle from whch ech consumer cn choose. Ths precpttes need for new technologes to provde consumers ccess to wht they wnt, when they wnt t, nd how they wnt t. To meet ths new requrement, the TV-Anytme Forum hs defned specfctons tht wll enle pplctons to explot locl persstent storge n consumer electroncs pltforms []. Although there re mny exstng TV-Anytme pplctons, such s Vrtul Chnnel nd EG (Electronc rogrm Gude), the group recommendton s ncresngly requred y the consumer. Group * Ths work ws supported y the Doctorte Foundton of Northwestern olytechncl Unversty of Chn. recommendton mens recommendng seres of TV progrms wth somethng n common to TV vewer. For exmple, user lkes the progrm he s currently vewng nd wnts to see more progrms lke ths one. TV-Anytme provdes MemerOf elements for ths usge scenro []. MemerOf mens group of whch the progrm s memer, such s <MemerOf xs:type="memeroftype" crd="groupcrd"/>. Wth MemerOf nd group CRID (Content Reference Identfer) [3], when progrm s enoyed y the user, the rodcster cn push other progrms elongng to the group, of whch the currently dsplyng progrm s memer, to the user. Further more, group for n entre seres would llow the DR (ersonl Dgtl Recorder) to cqure n entre seres of progrms y ust selectng one CRID to cqure. In order to cheve group recommendton, we should frstly clssfy the progrms nto groups, nd ssgn unque group CRID for t, nd then dd MemerOf element nto the progrm metdt. Clusterng, whch hs een wdely studed n dt mnng communty, s used to prtton dt set nto clusters so tht ntr-cluster dt re smlr nd nter-cluster dt re dssmlr [4]. But n rel pplctons there s very often no shrp oundry etween clusters so tht fuzzy clusterng s often etter suted for the dt. Memershp degrees etween zero nd one re used n fuzzy clusterng nsted of crsp ssgnments of the dt to clusters [5]. Fuzzy clusterng s ncresngly een ppled to dfferent technologcl felds, such s dt nlyss, unsupervsed lernng, nd mge recognton, etc. The pplcton of fuzzy cluster nlyss to pure text prtton s mture nd successful. However, fuzzy clusterng s rrely ppled n sem-structured dt clssfcton, such s XML documents. In TV-Anytme, the progrm descrpton metdt s represented wth XML, whch ncludes oth textul nd structured roceedngs of the Interntonl Conference on Informton Technology: Codng nd Computng (ITCC 04) /04 $ IEEE

2 nformton. In ths pper, we pply fuzzy clusterng to TV progrm clssfcton n TV-Anytme envronment. We frstly descre the XML sed progrm descrpton metdt representton. Secondly we present the progrm feture extrcton nd presentton method. Then we propose the method of usng fuzzy clusterng for progrm clssfcton.. rogrm Metdt Representton Generlly, metdt s dt out dt, such s the ttle, genre, nd lnguge of televson progrm. In the context of TV-Anytme, ech progrm hs metdt, whch cts s progrm descrpton. TV-Anytme uses the MEG-7 Descrpton Defnton Lnguge (DDL) to descre the metdt structure s well s the XML encodng of metdt. XML (extensle Mrkup Lnguge) [6], whch s developed y W3C, hs emerged s stndrd nformton exchnge mechnsm on the Internet. XML llows the encodng of structurl nformton wthn documents. The progrm descrpton metdt s correspondng to rogrminformtontle, the frst prt of content descrpton metdt n TV-Anytme. It descres tems of content, such s the ttle of the progrm, the genre t flls under, nd lst of keywords tht cn e used to mtch serch [7]. For nstnce, smple exmple of the progrm descrpton metdt s shown n Fgure. <?xml verson=".0"?> <rogrminformtontle> <rogrminformton> <BscDescrpton> <Ttle>Gone wth the Wnd</Ttle> <Synopss> Scrlett s frst mrrge ws for spte, nd her second mrrge ws for money, ut one mn kept wevng n nd out of Scrlett s lfe, the dshng cptn Rhett Butler. </Synopss> <Keyword>Love</Keyword> <Keyword>Mrrge</Keyword> < Keyword>Wr</Keyword> <Genre>Romnce</Genre> <ChnnelNo>cctv6</ChnnelNo> <StrtTme>00-09-T0:30:00+08:00</StrtTme> <Lnguge>en</Lnguge> </BscDescrpton> </rogrminformton> </rogrminformtontle> Fgure. rogrm descrpton metdt exmple In ths exmple, the progrm s move whose nme s Gone wth the Wnd. The synopss concludes textul descrpton of the progrm. The keywords re used to descre wht the progrm s out. The TV-Anytme gves genre dctonry [8], whch defnes the normtve TV-Anytme set of genres. For exmple, n the 3.4 secton of the genre dctonry, FICTION, there re ll together 9 knds of genres such s: Generl lght drm, Sop, nd Romnce etc. In DTV, there re multple chnnels smultneously rodcst to consumer, the chnnel numer s used to dentfy dfferent chnnels. The strt tme of the content mens the tme when the content s rodcst. Ths fctor s mportnt ecuse mye you won t get up n the smll hours of the dy; sy :30AM to wtch progrm even though you my show nterest n t n the other tme of the dy. The strt tme strng formt s complnt wth ISO 860 [9]. The lnguge of the content s lso mportnt to users. For exmple, news rodcst n Englsh my hve lttle or even no ttrcton to lttle oy who does not know Englsh t ll, ut t would e the desred content to young college student who wnt to mprove hs/her Englsh lstenng lty nd get nformed of the dly news t the sme tme. 3. rogrm Feture Extrcton We dopt the Vector Spce Model (VSM) [0] s the feture extrcton nd oect nformton presentton method. In the VSM prdgm, oect nformton s presented s vectors. In progrm metdt, there re some terms n Ttle, Synopss nd Keyword felds. We cn gther these terms n ll the progrm metdt y lphet s dctonry, nd represent t s vector. D ( term, term,... termn ) () To compute the dctonry vector, usully these steps re followed []. Frst the ndvdul words occurrng n the metdt re dentfed. Words tht elong to the stop lst, whch s lst of hgh-frequency words wth low content dscrmntng power, lke, re deleted. Then stemmng routne s used to reduce ech remnng word to word-stem form, tht s, the remnng words re reduced to ther stem y removng prefxes nd suffxes. For nstnce the words computer, computers, computng nd computlty could ll e reduced to comput. Ths s used for decresng redundncy. For exmple, through ove steps, we get dctonry vector s follows: D=(ccomplc, nm, cptn, clm, cup, dnger, dsh, enem, footll, fox, gone, ump, lke, lfe, love, mn, mrrg, mtch, mone, murder, musc, photo, spte, spy, vst, wr, wsh, wev, wnd, world) Wth the dctonry vector, we cn defne progrm s roceedngs of the Interntonl Conference on Informton Technology: Codng nd Computng (ITCC 04) /04 $ IEEE

3 two vectors nd : t, t,... t ) () ( n ( G, C, S, L) (3) s vector wth n (totl numer of terms n ove dctonry) tems, where t s the weght ssgned to term ( n ) n the dctonry vector. The weght t s ssgned complyng wth ths rule: f term s ncluded n Ttle, Synopss or Keyword feld of the progrm s metdt, then t =, otherwse t =0. For the flm Gone wth the Wnd, =(0, 0,, 0, 0, 0,, 0, 0, 0,, 0, 0,,,,, 0,, 0, 0, 0,, 0, 0,, 0,,, 0). mples wht the progrm s out. contns four tems: G, C, S, nd L, where G stnds for genre of the progrm; C stnds for chnnel numer; S stnds for strt tme of the progrm; L stnds for Lnguge. reflects rodcstng chrcterstcs of the progrm. The vlues n expresson (3) cn e gned s follows. TV-Anytme genre dctonry defnes totlly 0 second-level genres. We numer them from the frst genre to the lst genre wth to 0. So the G vlue of -th genre cn e ssgned s. For nstnce, snce Romnce s the ffty-ffth genre n the genre dctonry, so G(Romnce) s 55. We numer the chnnels wth postve ntegers. Relevnt chnnels re numered wth closer ntegers. For nstnce, n CCTV, there re chnnels, nd then we cn numer cctv to cctv wth to ; so C(cctv6) s 6. We cn dvde the strt tme of the progrms nto severl domns, nd numer them wth postve ntegers. The tme tht people hve more posslty to wtch TV wll e ssgned lrger ntegers. The prtton nd evluton s s follows: 0:00-4:00: ; 4:00-6:00: ; 8:00-:00: 3; 4:00-6:00: 4; 6:00-8:00: 5; 6:00-8:00: 6; :00-4:00:7; :00-4:00: 8; 8:00-:00:9; So S(0:30) = 9. There re 6703 ctegores of lnguge n the world wde ccordng to [] n 996. The lnguges cn e rnkng y populton spekng. The top 5 s s follows: Chnese North, Englsh, Spnsh, Bengl, nd Indn. We cn ssgn L vlue of the lnguge wth ts order n the rnkng. So L(en) =, en stnds for Englsh. Wth ove defnton, we cn get of the flm Gone wth the Wnd, =(55, 6, 9, ). 4. rogrm Clssfcton Usng Fuzzy Clusterng To llustrte the fuzzy clusterng process, we tke fve syntheszed progrm exmples, whose feture vectors re supposed s follows. rogrm ( Gone wth the Wnd ): =(0, 0,, 0, 0, 0,, 0, 0, 0,, 0, 0,,,,, 0,, 0, 0, 0,, 0, 0,, 0,,, 0), =(55, 6, 9, ). rogrm : = (, 0,, 0, 0,, 0,, 0, 0, 0, 0, 0, 0,, 0,, 0, 0,, 0,, 0,,, 0, 0,, 0, 0), =(59, 6, 5, ). rogrm 3: 3 = (0,,, 0, 0,, 0, 0, 0, 0,, 0, 0,, 0,, 0, 0,, 0, 0, 0,, 0, 0, 0, 0,, 0, 0), 3 =(0,, 7, ). rogrm 4: 4 = (0, 0,, 0,, 0,, 0, 0, 0,, 0, 0, 0,, 0,, 0, 0, 0,, 0,, 0, 0, 0, 0,, 0, 0), 4 =(85, 8, 3, 9). rogrm 5: 5 = (0, 0, 0, 0,,,, 0,, 0, 0, 0,, 0,,, 0,, 0, 0, 0, 0,, 0, 0,, 0, 0, 0, ), 5 =(, 5, 6, ). 4. Orgnl Dt Normlzton Snce dt n s not otned wth unversl mesurement, we should do normlzton for t. Frst, clculte the verge vlue nd stndrd devton of ech fctor n. _ 5 x x 5 _ (4) 5 S ( x x ) (5) Then, the orgnl feture vlue cn e normlzed to ts normlzed vlue. x _ x x (6) S Such normlzed vlue s not lwys on stndrd unverse, sy [0, ]. In order to compress the normlzed vlue nto [0, ], we need to dopt extremum normlzton equton to ccomplsh t. x x mn x (7) x mx x mn where roceedngs of the Interntonl Conference on Informton Technology: Codng nd Computng (ITCC 04) /04 $ IEEE

4 x mx mx( x, x,..., x ) (8) x mn mn( x, x,..., x ) (9) Through ove processng, we get normlzed vlue of n tle. Tle : Normlzed vlue of rogrm ID G C S L Buldng Fuzzy Smlrty Mtrx In fuzzy smlrty mtrx, r s the smlrty etween oect nd. A commonly used smlrty metrc s the cosne of the ngle etween two oects. m x k x k k r m m ( x k )( x k ) (0) k k We cn get two fuzzy smlrty mtrces, one s R, whch s clculted from, nd the other s R, whch s clculted from. Snce the two mtrces re symmetrc, we ust gve the lower trngle prt of ech mtrx. When clcultng R, we use the normlzed dt n Tle R R Wth these two mtrces, we cn construct our fnl fuzzy smlrty mtrx R ccordng the followng equtons. R W R W R where () W + W = ( 0 W, 0 W ) () W mens the weght of R, nd W mens the weght of R. The fnl fuzzy smlrty mtrx cn e clculted s (Supposng W =0.6, W =0.4): R Clusterng Although the fuzzy smlrty mtrx hs reflexvty nd symmetry, t hs not trnstvty. If relton hs reflexvty, symmetry, nd trnstvty, we defne t s n equvlence relton. Only when the relton s n equvlence relton, set X cn e clssfed ccordng to t. Usng equvlence relton for clssfcton, n element elongs to one nd only one cluster. (n) The fuzzy equvlent mtrx R cn e clculted y usng Wrshll lgorthm [3], whch s outlned n the followng steps: * * * (0) set R [ r ], R R ; () k ; * * * * () r r ( rk rk ) ; (3) k k ; (4) If k n, go to (), otherwse ext. * (n) Now, we get R, R = R * R (n) Assgnng the threshold wth dfferent vlue, we cn get dfferent clusterng result to the progrm set. The clusterng result s shown n Tle. roceedngs of the Interntonl Conference on Informton Technology: Codng nd Computng (ITCC 04) /04 $ IEEE

5 Tle : Clusterng result Threshold ( ) Clssfcton (rogrms n pr of rces elong to one cluster) 0.55 {,, 3, 4, 5} 0.60 {, 3, 4, 5}, {} 0.64 {, 3, 4}, {}, {5} 0.75 {}, {}, {3}, {4}, {5} From the clusterng result, we cn see tht f s too smll, ll progrms wll elong to one cluster; f s too lrge, ech progrm wll elong to ndvdul clusters. Here, we choose =0.64, nd the progrms re prttoned nto 3 clusters. We set group CRID of cluster {, 3, 4} s groupfcton. Snce Gone wth the Wnd elongs to ths cluster, we cn dd MemerOf element to ts metdt. The revsed metdt s shown n Fgure. <?xml verson=".0"?> <rogrminformtontle> <rogrminformton> <BscDescrpton> <Ttle>Gone wth the Wnd</Ttle> <Lnguge>en</Lnguge> <MemerOf xs:type="memeroftype" crd="groupfcton"/> </BscDescrpton> </rogrminformton> </rogrminformtontle> Fgure. rogrm metdt wth MemerOf element S004v, TV-Anytme Forum, Jun. 00 [4] Zheng Zhk, Zhng Gungfn nd Sho Huhe. Dt Mnng nd Knowledge Dscovery: An Overvew nd rospect. Informton nd Control, 999, 8(5): [5] Frnk Höppner. Fuzzy Cluster Anlyss. -clusterng.de/, Aug. 00 [6] T Bry, J ol, nd C M Spererg-McQueen, Extensle Mrkup Lnguge (XML).0, REC-xml, Oct. 000 [7] TV-Anytme System Descrpton Document (Informtve wth mndtory ppendx B), WD608, TV-Anytme Forum, Aug. 00 [8] TV-Anytme Metdt Specfctons Document, S003v rt A AppendxB, TV-Anytme Forum, Jun. 00 [9] ISO 860, Dt elements nd nterchnge formts - Informton nterchnge - Representton of dtes nd tmes. [0] G Slton. Automtc Text rocessng: The trnsformton, nlyss, nd retrevl of nformton y computer. Addson-Wesley, Msschusetts, USA, 989 [] Tk W Yn nd Hector Grc-Moln. Index Structures for Informton Flterng Under the Vector Spce Model, roceedngs of the Tenth Interntonl Conference on Dt Engneerng, Houston, USA, 994 [] Lnguges n the World. g/sdyy.htm [3] Stephen Wrshll. A theorem on Boolen mtrces. Journl of the ACM. 96, 9(): -. [4] GroupLens Home ge, ml-dt.tr.gz 5. Conclusons Ths pper ntroduces our reserch nd desgn on pplcton fuzzy clusterng to TV progrm clssfcton n TV-Anytme envronment. Wth progrm clssfed on rodcster sde, the progrm group recommendton ecomes possle. The dt sets used for expermentton nd performnce nlyss s the MoveLens dtset [4], whch conssts of totl of 68 move descrptve nformton. The clusterng result proves tht doptng fuzzy cluster nlyss n progrm clssfcton s fesle nd effectve. References [] TV-Anytme Requrements on Envronment, TV035r6, TV-Anytme Forum, Aug. 000 [] TV-Anytme Specfcton on System Descrpton, S00v3, TV-Anytme Forum, Fe. 003 [3] TV-Anytme Specfcton on Content Referencng, roceedngs of the Interntonl Conference on Informton Technology: Codng nd Computng (ITCC 04) /04 $ IEEE

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