Re-Ranking Retrieval Model Based on Two-Level Similarity Relation Matrices

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1 Intenatonal Jounal of Softwae Engneeng and Its Applcatons, pp Re-Rankng Reteval Model Based on Two-Level Smlaty Relaton Matces Hee-Ju Eun Depatment of Compute Engneeng, Chonbuk Natonal Unvesty 56 Baeke-daeo, DuckJn-Gu, Jeonu, Koea Abstact Web-based specalzed eteval systems fo scentfc felds extemely estct the expesson fo use's nfomaton equests. Theefoe the pocess of nfomaton content analyss and that of the nfomaton acquston become nconsstent. In ths pape, we apply the fuzzy eteval model to solve the hgh tme complexty of the eteval system by constuctng a educed tem set fo the tem's elatvely mpotant degee. We also pefom a cluste eteval to eflect the use's quey exactly though the smlaty elaton matx satsfyng the chaactestcs of the fuzzy compatblty elaton. Ths pape poves the pefomance of a poposed e-ankng model based on the unon of smlaty of the fuzzy eteval model and the document cluste eteval model. Keywods: Smlaty Relaton Matx, Tme Complexty, Concept Infomaton, Cluste, Thesauus, Membeshp functon, Reducton Tem, Re-ankng Reteval Model 1. Intoducton The development of web sevce applcaton technology and the spead of Intenet have enabled nfomaton to ncease geometcally, enablng uses to access and stoe lots of nfomaton easly. Pesently, web based academc feld (heenafte efeed to as 'documents') eteval system apples a keywod matchng technque that povdes a esult of eteval by expessng the use's nteest nto a quey as a fom of natual language. Infomaton eteval model oganzes an enomous amount of nfomaton about vaous subects and t should allow uses to access the nfomaton they need quckly. Use oented eteval systems should expess the use's nteest and equement fo nfomaton accuately and have a mechansm that ensues that pocesses of nfomaton acquement and analyss of content of documents ae consstent so that a esult of etevng documents can satsfy uses. Ths pape poposes a e-ankng model that eflects a smlaty based on content between use quey tems and keywod of documents. Ths pape solves the dependence poblem between expesson of degee of elatve concept of tems and keywods by applyng fuzzy elaton concept and usng smlaty and concept dstance between tems that contan concept nfomaton. Hgh tme complexty of system s pocessed by usng educed tem set. In othe wods, e-ankng s composed of the subect analyss phase that extacts keywod to pesume an ntenton of authos fom documents, and the equement analyss pocess that eteves elevant documents effcently by accuately undestandng the use's nteest. Subect analyss mechansm s povded by establshng the thesauus and smlaty elaton matx. The analyss of use's nteest ntoduces algothm that makes a model of seekng quey expanson. Ths pape consst of fve chaptes oganzed as follows: Chapte descbes the pesent studes on a subect analyss pocess and a seach model, Chapte 3 poposes a educed tem set that expesses doman knowledge, a subect analyss mechansm that ISSN: IJSEIA Copyght c 015 SERSC

2 Intenatonal Jounal of Softwae Engneeng and Its Applcatons ceates a fuzzy thesauus and smlaty elaton matx, a seach algothm that uses a fuzzy thesauus, and a smlaty elaton matx. Chapte 4 tests and evaluates the poposed algothm, and Chapte 5 pesents conclusons and futue studes. Relevant Reseach.1. Concept Netwok A concept netwok s composed of nodes and lnks. Each node expesses a concept o a document. Lnks expess meanng elatons among concepts and elatons between documents and concepts. Fgue -1 shows a concept netwok that expesses a set of concepts C { c, c,, c } whch s composed of a set of documents 1 7 D { d, d, d, d } and keywods whch expess meanng connecton among concepts. d 1 d d 3 d c 1 c 4 c c c c c 7 Fgue -1. Concept Netwok Concept netwok F(C) fo a set of concepts C c, c,, } can be expessed as { c 1 7 follow F C ) { c, c, w c, c C, w [0.1]} Whee n elaton, ( 1 1, 1 1, c, c, w F ( C ) c has c as hyponym and w means a degee of fuzzy 1 1, 1 1 elatons between two concepts. In Fgue [-1], a document d s d, c,0. 5, 1 d, c, 1, d, c,0.8 F ( c ). In Fgue [-1], elatons between subsets that 4 5 ae connected n tems of meanng fom above mentoned elatons use tanston popety (fuzzy executon elaton). [1,] Fo example, c c, w, c, c, w, c, c, w, c, c, w F ( ) and theefoe c c, w F ( ), C 1 1, 1 3 1,3 4, , 4, C , 4 1, 4 w whch ndcate a degee of fuzzy elaton between c and c can be found by usng the followng Zadeh fuzzy expanson logc (fomula ). w max{mn( w, w ), mn( w, w )} -- (Fomula -1)., l, k, l k In Fgue [-1], f use quey s Q {( c,1.0 )}, eteval state value (RSV) of d s evaluated by the followng thee pocedues usng Zadeh's tanston elaton. 1) Because of c c d, then mn(, w ) mn( 0.7,0.5 ) ,1 1, w. ) Because of c c c d, then mn(, w, w ) mn( 0.8,0.9,1) , 3 3, 4, w. 3) Because of c c c d, then mn(, w, w ) mn( 0.8,0.6,0.8 ) , 3 3,5 5, w. 350 Copyght c 015 SERSC

3 Intenatonal Jounal of Softwae Engneeng and Its Applcatons Reteval state value (RSV) of d s evaluated as max( 0.5,0.8,0.6 ) 0. 8 and pesented to uses. Retevng documents that ae coelated wth use quey conceptually though concept netwok that expesses meanng elatons between doman concepts wth a degee of fuzzy can mpove ecall. [3, 4] Howeve, the above mentoned method has a weakness that t s dffcult to be bult, mantaned, and appled because ts concept netwok s mantaned manually by expets. To solve the afoementoned poblem, t s necessay to automate the concept netwok and suppot vaous eteval equements so that assocatve eteval s possble n ode to enhance effcency of eteval. Thee ae eteval technques that use fuzzy elatons and automatc quey expanson, whch s based on the smlaty elaton thesauus as epesentatve studes on concept netwok, subect analyss and eteval model. Howeve, the above mentoned studes have a weakness that the subect analyss mechansm and mechansm whch establshes eteval model fo analyzng use equement lack consstency and accuacy n eteval pefomance... Impoved BK-fuzzy Infomaton Reteval Model (A-FIRM) AFIRM (Advanced bandle-kohout Fuzzy nfomaton Reteval Model) automated tem elaton thesauus by buldng a souce document base though statstcal and pobablty technque and usng fuzzy elatons and fuzzy elatonal poducts. [5-7] AFIRM povdes elaton equest (R-equest) opeaton that expands tem concepts though tem elaton thesauus and fuzzy seach equest (FS-equest) opeaton that ntepets and seaches use quey. Fgue - shows A-FIRM that extacts aea dependent knowledge by automatcally establshng document base and thesauus though document based geneato and thesauus geneato and gasps the ntenton of an autho who povdes nfomaton. Fo eteval equement pocess, quey expanson method that makes a fuzzy composton of thesauus and quey tems was adopted. Thesauus and document base ae ceated by extactng a set of educed tems to mnmze eteval opeaton of tme complexty. T e m s S e t S e t A n a ly ze A n a ly ze R e d u c e d T e m R e d u c e d T e m S e t G e n e a to S e t G e n e a to S o u c e D ocum ent-base R e d u c e d T e m S e t T h e sa u u s T h e sa u u s G e n e a to G e n e a to D ocum ent- base D ocum ent- base G e n e a to G e n e a to T h e sa u u s D ocum ent-base R e la to n R e q u e st R e la to n R e q u e st R e t e v a l U se Q u e y Q u e y E x p a n d e Q u e y E x p a n d e S e a c h e S e a c h e R e t e v a l R e t e v a l R e q u e st R e q u e st Fgue -. System stuctue of A-FIRM A-FIRM has an advantage that t can mpove ecall facto by solvng hgh tme complexty and povde poty of document eteval esults. Howeve, t s mpossble to expect an mpovement of pecson ato that uses feel because a standad fo a set of Copyght c 015 SERSC 351

4 Intenatonal Jounal of Softwae Engneeng and Its Applcatons educed tems has not been povded and a pepaaton fo adustment of eteval esult poty s smple. 3. Algothm fo Document Re-ankng Ths pape poposes a way to establsh thesauus and smlaty elaton matx and algothm fo document e-ankng based on knowledge of ttle and abstact fo etevng nfomaton on academc felds Ceaton of Document Base Usng Sgmod Functon In ode to oganze the souce document base, extact tems fom document set and expesses elaton between tems and documents wth weght. Ths pape made meanng elaton egadng a specfc document fuzzed as a bass fo hypothess that fequency of appeaance of tems epesents the contents of a document [5, 9]. Fo ths pupose, sgmod functon, whch s S shaped nonlnea functon, and fuzzy membeshp functon s used. In a case that keywod extacted fom a document occus n ttle T of a document o a set of keywod (K ), mpotance of keywod n a document egadng fequency of occuence of keywod can be found by usng sgmod functon 1 shown n Fgue [3-1]). In a case whee ndex occus n a summay of a document, a degee of belongng to fequency can be found by usng sgmod functon shown n Fgue 3-. [8, 13] F F 6 Fgue 3-1. Membeshp Functon Fgue 3-. Membeshp Functon ( ) ( ) 1 Sgmod functon abstacts elatve pobablstc fequency to absolute possblty. Ths pape poduced the weght of the fnal keywod that epesents a document by applyng mn-max opeaton to the weght of an aea that occus, and eseach shown n [8] n ode to poduce the mpotance degee of fequency accodng to aeas that occu and calculate mpotance degee about the oveall document. w T A T K A K max{mn(, ), mn(, ), mn(, )} (Fomula 3-1) T : The mpotance degee of ttle aea ove keywod n a document A : The mpotance degee of summay aea ove keywod n a document K : The mpotance degee of keywod aea ove keywod n a document 35 Copyght c 015 SERSC

5 Intenatonal Jounal of Softwae Engneeng and Its Applcatons In accodance wth (Fomula 3-1), souce document base, whch s fuzzy elatons between document set ( D ) and keywod set, (T ) s expessed as follows. In the above fomula, n s the numbe of the document set and m s the numbe of keywods extacted fom the document set. In ths pape, a manual ndex s adopted as a pocess that extacts ndex. Adopted man pespectve s to popose a quey expanson model that extacts knowledge and suppots content based eteval by establshng thesauus and smlaty elaton matx that ndcates subodnaton of extacted keywod athe than automatc ndex n tems of meanng. [11-1] 3.. Tme Complexty and Reduced Tem Matx Ths pape poposes a way that constucts smlaty elaton thesauus based on educed tem matx, whch s dependent on doman. (1) Souce document base and educed tem matx Reduced tem set uses souce document base to poduce and pocesses hgh tme complexty poblem. Ths pape equates the elaton between two fuzzy sets as the fuzzy membeshp functon as follows to apply Boolean algeba to fuzzy set. t A B ( w ) A ( w ) max{mn( ( w ), ( w ), mn( 1 ( w ),1 ( w ))} A 1 d B A W W k w w ( D ) (Fomula 3-) d k 1 : The membeshp degee that element w wll belong to fuzzy set A : The membeshp degee that element w wll belong to fuzzy set B t : Degee of elaton that keywod has n document set (doman) ( w ) B d : The numbe of all documents ( D w w k ) : The smlaty degee between and n a document k B Ths pape evaluates a degee of elaton that each keywod has n a doman by usng the membeshp functon shown n (Fomula 3-). Reduced tem set based document base whch expesses fuzzy elaton between documents and educed tems s composed of only educed tems extacted fom the souce document base that can epesent contents of a document R. Whch educed tems set s as follows. Copyght c 015 SERSC 353

6 Intenatonal Jounal of Softwae Engneeng and Its Applcatons () Smlaty elaton thesauus Thesauus that expesses fuzzy elaton degee among keywods based on document base that s composed of educed tems uses fuzzy elatonal poduct of document base and souce document base as shown n (Fomula 3-3). S R T R S, 1 n (mn( w n, I n ), (mn( 1 w n ), (1 I n ))) (Fomula 3-3) S : Smlaty degee between keywod and educed tem w : Impotance degee of keywod n document n n I : Impotance degee of educed tem n educed matx document n n Fuzzy elatonal poduct opeaton consdeed smultaneous appeaance fequency of (Fomula 3-3) unde the assumpton that the moe smultaneous appeaance fequency among keywods n a specfc document s, the moe smla keywod s. Smlaty thesauus S s as follows. (3) Smlaty elaton matx Ths pape defnes smlaty elaton matx that satsfes fuzzy toleance elaton as shown n (Fomula 3-4) as a way to eadust eteval ankng. Ths s a way to consde keywod nfomaton that has been omtted n documents and satsfy fuzzy toleance elaton based on souce document base and smultaneous appeaance. S R S T R, 1 m (mn( w m, I m ), (mn( 1 w m ), (1 w m ))) (Fomula 3-4) S : The smlaty degee of keywod and keywod w : Impotance degee of keywod n a document m n souce document base m Smlaty elaton matx S s as follows. 354 Copyght c 015 SERSC

7 Intenatonal Jounal of Softwae Engneeng and Its Applcatons 3.3. Expanson of Quey usng Fuzzy Relaton (1) Expesson of use quey Usng smlaty elaton matx to expand doman knowledge fo quey tems expands quey. Quey opeato s defned as follows. [13-14] 1) x ORx x ) ( x ) ( ) x ANDx x ) ( x ) ( 3) NOTx x ) 1 ( x ) (Fomula 3-5) ( 4) VERYx q ( x )) ( x ) ( vey 1 / 5) FAIRYx q ( x )) ( x ) Whee, ( fay x, x [0,1],1 n, 1 n () Smlaty elaton thesauus based quey expanson Quey on use nfomaton demand conssts of quey base, whch has been expanded by thesauus and fuzzy compostons to expand doman knowledge. Quey base ( Q ) s poduced by fuzzy composton among document base S that ndcates fuzzy elaton quey Q, educed tem set and document whch has been expessed by uses as shown n (Fomula 3-6). Q Q S Q S ( x, z ) Max { Mn { ( x, y ), ( y, z )}} y Y Q S (Fomula 3-6) ( x, y ) : Relaton between use quey and keywod Q ( x, y ) : Degee of elaton between document and educed tem set (thesauus) Q ( x, y ) : Degee of elaton between use quey and educed tem set Q Q : Quey expanson set Q q, q, q, q } (3) Smlaty evaluaton { 1 3 Document eteval state value (RSV) can be found by evaluatng the smlaty between quey base and document base. Smlaty measue method s defned as follows as shown n (Fomula 3-7). O Q R T { RSV ( d ), RSV ( d ), RSV ( d 1 n )} ( RSV I R ) Q : ( d ) t (1 I q ( ) ' ' and 1,,, n k k Q ) (Fomula 3-7) Degee of elaton between document and educed tem n educed tem set : degee of elaton between use quey and educed tem d : eteval state value (RSV) of document Copyght c 015 SERSC 355

8 Intenatonal Jounal of Softwae Engneeng and Its Applcatons 3.3. Cluste Reteval though Document Relaton Exstng method of usng thesauus s to expand the eteval aea and can pecson and ecall of document eteval. Howeve, exstng methods ae not enough to eteve nfomaton accuately. Theefoe, ths pape poposes a way to expand quey concept by usng fuzzy smlaty elaton matx as a way to expand knowledge as shown n Fgue 3-3. T R T R S S m la ty e la to n M a tx R T R C lu ste n g R F u z z y e te v a l th e sa u u s S R Fgue 3-3. Reteval by Fuzzy Smlaty Relaton Quey constucted by uses gasps use equements dependng on elaton of quey tems and knowledge about quey tems expands to detaled knowledge fom compehensve knowledge. As uses have consdeable knowledge about specfc domans, system should be desgned n such a manne that t can expess vaous use equements and compehensve meanng eteval accuately [1-13]. Ths pape classfes a toleance class that satsfes toleance elaton n smlaty elaton matx poduced based on souce document base and adds t to souce quey to mpove ecall. In othe wods, quey weght, whch s added by expandng souce quey that eflects knowledge of doman, s assgned by usng fuzzy expanson pncple by tanston elaton. Documents can be evaluated dependng on document state value though document cluste. Only pat of souce document base ( R ), whch has been evesed, s used to evaluate smlaty. O Q R { RSV ( d ), RSV ( d ), RSV ( d )} e 1 n RSV ( d ) t (1 w q ( ) ' ' and 1,,, n k Qe ) (Fomula 3-8) Q : Quey base expanded by smlaty elaton matx ( S ) e w : Degee of elaton between documents n souce document base ) ( R and keywod 3.4. Document Re-ankng Algothm though Smlaty Combnaton Reteval state value ( O ) of each document though thesauus that has been poduced based on educed ten set, and eteval state value ( O ) of each document that has been poduced based on souce document base mean eteval state value about fst quey 356 Copyght c 015 SERSC

9 Intenatonal Jounal of Softwae Engneeng and Its Applcatons expanson and second content quey espectvely. Ths pape mantaned ecall by fst phase quey expanson and conducted second phase smlaty elaton matx based cluste eteval to enhance pecson. Ths pape eadusts eteval state value so that pope documents can be eteved n a hgh ank though e-ankng. O sm combned O O RSV ( d ), RSV ( d ), RSV ( d )} (Fomula 3-9) { 1 n : Reduced tem matx based fuzzy eteval (document state value) O : Smlaty elaton matx based cluste eteval (document state value), : Set to 1 Sm combned : Result of e-ankng of document state value Fgue 3-4. Document Re-ankng Reteval Model 4. Expement and Evaluaton Pocedues fo evaluaton of pefomance of eteval e-ankng model ae as follows. Fst, souce document base s constucted by applyng keywod and appeaance fequency to sgmod functon. Second, educed tem set and thesauus ae constucted and fuzzy eteval s conducted though quey expanson. Thd, smlaty elaton matx s constucted based on souce document base and eteval s conducted n accodance wth the degee of classfcaton. Fouth, ecall and pecson of e-ankng model s evaluated Method of Analyzng Expement Rankng pecson shown n (Fomula 4-1) and ankng ecall shown n (Fomula 4-) have been used to measue eteval effcency of e-ankng model poposed n ths pape [4]. Copyght c 015 SERSC 357

10 Intenatonal Jounal of Softwae Engneeng and Its Applcatons Match Doc Doc n ode Match Doc _ pecson (Fomula 4-1) Rank Doc n : The numbe of documents whose ae consstent wth test quey esult Rank Doc n Rank : Documents whch belong to uppe ( n ) among document ankng decson esult KT Set match ode KT Set match _ ecall (Fomula 4-) Rank match : Numbes of documents about test quey esult n KT-Set Rank : Rankng at the moment that all quey esult documents ae ncluded n KT Set match match 4.. Subects of Expement Documents whch ae coveed by ths pape ae 1000 documents (document no. 1~1000), whch ae stuctued n ttle, summay, and keywod sets n all documents of KT-Set (.0). KT-Set (.0) quey set s composed of a total of 50 quees, but test quey whose documents ae sutable fo quey ae fve o moe s extacted to enhance elablty of expement esult as shown n Table 4-1. quey numbe Table 4-1. Statstc Infomaton of Expement Set numbe of quey language(content of quey) Numbe of sutable documents KT Set match cluste classfcaton ctea value( ) 8 (multmeda & database) (automatc tanslaton machne tanslaton) (chaacte ecognton scpt ecognton) (ntellgence type & nfomaton eteval) (geogaphc nfomaton) (voce ecognton voce ceaton) (supe hgh speed & nfomaton & communcaton netwok) (neual netwok & fuzzy contol) (thesauus mopheme) (sample samplng) Compason and Evaluaton of Expements (1) Test of the efeence value of assotment Ths test evaluated the pecson ato accodng to changes n the efeence value of assotment ( ) n the pocess of document cluste method. The sutablty of assotment was used to dynamcally assot the ndex tem knowledge n ode to mpove the dscmnaton fo smla document sets. 358 Copyght c 015 SERSC

11 Intenatonal Jounal of Softwae Engneeng and Its Applcatons Fgue 4-1. Rankng Pecson Accodng to Classfcaton Ctea Value ( ) Dung the test, when the efeence value of the assotment n the document cluste method ( ) was , the ank pecson ato was 0.5. The best pecson ato was pesented when the efeence value of assotment was Fo the efeence value of assotment ove 0.8, the pecson and ecall ato wee 1 and 0.4, espectvely. Consequently, ths pape used a value of 0.6 fo the efeence value of assotment n ths test. () Results of the compason and evaluaton As shown n Fgue. 4-, the esults pesented that the aveage ankng ecall and pecson ato wee 0.81 and 0.86, espectvely, n the test of document ankng method poposed by Pesn. The aveage ankng ecall ato was 0.89, but the aveage ankng pecson ato was 0.8 n the test of the document ankng method poposed by Hee-Ju, Eun [13]. In addton, the aveage ankng ecall and pecson ato wee 0.85 and 0.87, espectvely, n the test of document ankng method poposed by Koczy [ 4]. Moeove, ths test evealed that the ankng pecson and ecall aton, whch pesented dawbacks n a fuzzy eteval and document cluste method, wee ove 0.9, espectvely. Ths means that the pecson ato of the fuzzy eteval nceased whle the ecall ato of the fuzzy eteval was mantaned 5. Concluson Fgue 4-. Aveage Rankng Recall and Pecson Web based document eteval system, whch s beng used at pesent, s weak n selectng documents whee the use's nteest has been eflected a lot fom nfomaton that has been acqued. Ths pape povded a mechansm of nfomaton acquston pocess fo nfomaton equements and content analyss pocess so that eteval systems can satsfy the use's nfomaton equement. In addton, ths pape s to expand Copyght c 015 SERSC 359

12 Intenatonal Jounal of Softwae Engneeng and Its Applcatons quey language by abstactng a degee of smlaty between quey language that expesses the use's nteest and keywod extacted fom documents nto fuzzy value by usng smlaty degee and concept dstance of tems to whch concept nfomaton belong. Ths pape s composed of e-ankng model though a combnaton of fuzzy eteval, document cluste technques, and smlaty. Reduced tem set n fuzzy eteval was constucted to deal wth hgh tme complexty. Ths pape coveed only tems that satsfy cut though mn-max opeaton of mpotance degee of keywod. Ths pape s to enhance eteval speed and pecson though a document cluste technque that expands seach tem set to eteve documents wheen use equements have been eflected. Document cluste technque eteved documents by expandng to tems that satsfy toleance elaton of classfcaton ctea value ( ) o hghe based on smlaty elaton matx about use quey. Document eteval ankng s eadusted by combnng the smlaty degee of a document cluste technque that clustes documents that ae connected to the chaactestcs of ecall of fuzzy eteval so that use nfomaton equements can be satsfed. Evaluaton of e-ankng model though combnaton of smlaty poposed n ths pape showed that pecson and ecall wee 0.9 o moe, whch suggests that pecson was mpoved whle ecall of fuzzy eteval was mantaned. Refeences [1] H. Y and L. Wene, Document sentment classfcaton by explong descpton model of topcal tems, Compute Speech & Language, vol. 5, no., (010), pp [] Y. Pe and G. We, A lnk-bdged topc model fo coss-doman document classfcaton, Infomaton pocessng & Management, vol. 49, no. 6, (013), pp [3] CR. Chowdhuy, Infomaton eteval usng fuzzy c-means clusteng and modfed vecto space model, Compute scence and nfomaton technology, (010). [4] L. T. Koczy and T. D. Gedeon, Infomaton eteval by fuzzy elatons and heachcal cooccuence, Pat 1, Pat. TR 99-01, UNSW, (1999). [5] H. Y and L. Wene, Document sentment classfcaton by explong descpton model of topcal tems, Compute Speech & Language, vol. 5, no., (010), pp [6] P. Su, C. Shang and Q. Shen, A heachcal fuzzy cluste ensemble appoach and ts applcaton to bg data clusteng, Jounal of Intellgent and Fuzzy Systems, vol. 8, no. 6, (015). [7] W. Ke, Infomaton-theoetc tem weghtng schemes fo document clusteng and classfcaton Intenatonal Jounal on Dgtal Lbaes, vol. 16, no., (014), pp [8] S.-W. Han, A Document Classfcaton Algothm Usng the Fuzzy Set Theoy and Heachcal Stuctue of Document, ICCSA, no. 1, (004), pp [9] S. J. Song, A Study on Impovng the Pefomance of Document Classfcaton Usng the Context of Tems, Jounal of KSIM, vol. 9, no., (01), pp [10] J. Km, and M. Km, A Study on the Implementaton of SNS Message Classfcaton by Emoton Factos, The Jounal of the Insttute of Boadcastng Communcaton, vol. 11, no. 4, (011), pp [11] T. Basu and C. A. Muthy, Towads enchng the qualty of k-neaest neghbo ule fo document classfcaton, Intenatonal Jounal of Machne Leanng and Cybenetcs, vol. 5, no. 6, (013), pp [1] G. Bodogna, Soft clusteng fo nfomaton eteval applcatons, Wley Intedscplnay Revews : Data Mnng and Knowledge Dscovey, vol. 1, no., (011), pp [13] H.-J. Eun, An Algothm of Documents classfcaton and Quey Extenson usng fuzzy functon, Jounal of KISS : Softwae and applcatons, vol. 8, no. 3 (001). [14] G. J. Kl and B. Yuan, "Fuzzy Sets and Fuzzy Logc Theoy and Applcatons", (1998). 360 Copyght c 015 SERSC