Multi-View Data Visualization Based Big Data Analysis Using Clustering Similarity Measure

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1 Mult-Vew Data Vsualzaton Based Bg Data Analyss Usng Clusteng Smlaty Measue Snvasa Rao Madala #1, V N Rajavaman #2, T.Venkata Satya Vvek #3 1,3 Reseach scholos, Depatment of Compute Scence & Engneeng, D M.G.R Educatonal And Reseach Insttute Unvesty, Chenna, Inda. 2 Pofesso, Depatment of Compute Scence & Engneeng, D M.G.R Educatonal And Reseach Insttute Unvesty, Chenna, Inda. 1 m.snu13@gmal.com, 2 najavaman2003@gmal.com, 3 tvsvvek1990@gmal.com Abstact ---In bg data, data vsualzaton s an mpessve concept to epesent data fo effcent data analyss to handle hgh dmensonal data. In data vsualzaton, thee ae thee man popetes ) to epesent wthout loss of data pattens ) wthout any changes n data patten change the attbutes ) data vsualzaton wth stuctue and unstuctued data attbutes fo data analyss. Thee ae many types of data vsualzaton ae pesented pactcally to defne data analyss (.e. topc based data vsualzaton, attbute based data vsualzaton, audo based data vsualzaton and text based data vsualzaton n dffeent data sets). Paallel co-odnate s an effcent and effectve data vsualzaton tool to analyze and handle mult attbute hgh dmensonal data. It s based 5Ws densty sendng and ecevng data vsualzaton, t also ead data pattens and attbutes wth educes the ovelappng to data pattens. Smlaty measue s a categozaton popety to epesent data wth elatonshp objects n data set evaluaton wth dffeent pa of attbutes. We need to mpove paallel coodnate tool to suppot mult-attbute object elatons, so we popose and mplement novel method.e. (Smlaty Measue Centeed wth Mult Vewpont (SMCMV)) appoach and elated clusteng appoaches to epesent data. Usng mult-vewpont, we can acheve assessment based smlaty ndex wth data vsualzaton. Usng mult vewpont, we pesent theoetcal analyss based on mult attbutes pesentaton. Ou expemental esults gves best data epesentaton n data vsualzaton wth effcent smlaty measue on eal tme document evaluaton wth dffeent known collected clusteng appoaches. Key wods: Data Vsualzaton, Paallel Co-odnate, Multvaate attbutes, Smlaty Measue and Mult vewpont and Clusteng Methods. I. INTRODUCTION Stuctued and unstuctued data n bg data vsualzaton contans dffeent foms of data lke mage, audo and vdeo and collected ths data fom dffeent multple data sets based on the sze tme and space complexty evaluaton. Fo example Face book geneates 25 GB of data whch contans followng use s pesonal detals and the shang data wth mutual and pesonal fends. Thousands, hundeds of dffeent dmensonal attbutes by monthly povdng dffeent data to analyze multple attbute dmensons to handle data vsualzaton. Because of nceasng apd usage of bg data n vaous applcatons, dffeent authos poposed dffeent assocaton and classfcaton and clusteng to analyze hgh dmensonal data. Paallel coodnate data vsualzaton s one of the pomsng appoaches to epesent data wthout change the data pattens fom oveall data. Sample data vsualzaton wth dffeent dmensons as shown fg 1. Fg.1. Paallel coodnate plot of the data vsualzaton fo flea data. DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

2 Neghbo attbute pattonng s as shown n fg 1 wth dffeent node axes values n dmensonal set. Some of the eseaches and ntoduced to defne effectve data vsualzaton wth dffeent node data pesentaton wth same attbutes. Manly data vsualzaton conssts thee data epesentatons n eal tme data pesentatons, topc based data vsualzaton, whch consst about patcula topc wth algothm pocess lke netwok taffc vsualzaton cloud data vsualzaton. Data type based data vsualzaton, whch conssts accuate type of data lke text based data vsualzaton, audo and vdeo data vsualzaton wth dffeent fomatons. Data set vsualzaton, whch conssts patcula data sets lke socal and netwok, oented data sets wth dffeent data pattens. To epesent data n these thee ways, tadtonally develop Paallel coodnate 5W s densty model, n that analyze data attbutes and epesent those attbutes n paallel axes pesentaton fo multple data set wth data types and topcs evaluaton n eal tme data set pesentaton. To measue smlaty between attbutes n data vsualzaton wth elatonshps then paallel coodnate 5W densty pesentaton not satsfed n data evaluaton. So n ths pape, we popose and develop novel Smlaty Measue Centeed wth Mult Vewpont (SMCMV)) appoach and elated clusteng appoaches to epesent data. Ths appoach follows mult-vew data epesentaton wth dmensons n attbute elatonshp. In that clusteng s an aggessve concept and topc n data eteval based on attbutes, n that we ntnsc the stuctue data fomaton and fomulate them nto equed and meanngful data pesentaton. So ou poposed appoach follows clusteng popetes to ead and pesent data n dffeent dmensons wth attbute elatons. And also ou appoach exclusvely follows multvew data pesentaton wth espect attbute pesentaton. We also calculate smlaty measue n attbute pattonng n data set exploaton; smlaty measue plays mpotant aspect n success and falue of data epesentaton n clusteng pocedue. Man objectves of ou poposed appoach as follows: 1. We pesent and popose an appoach to fnd the smlaty between data objects wth dffeent elatons n hgh dmensonal data evaluaton. 2. Poposed smlaty measue wth dffeent clusteng calculatons wth povable qualty and pefomance consstent. 3. Dsplay mult-vew data vsualzaton wth dffeent data pattens. 4. Gve effcent data vsualzaton wth multple attbutes wth clusteng calculatons. Remanng of ths secton oganzed as follows: Secton 2 elates elated wok about vsual data pesentaton technques, secton 3 dscuss about paallel coodnate densty model wth data vsualzaton. Secton 4 descbes poposed appoach.e. SMCMV and t s mplementaton pocedue. Secton 5 fomalzes the computatonal and pefomance evaluaton of poposed appoach wth eal tme data sets and plot the esults, secton 6 concludes oveall concluson. II. RELATED WORK Compaatve compose plots, as a standout amongst the most pomnent stateges, was fst suggested by Inselbeg [3] and Wegman ecommended t as somethng fo extaodnay vewpont data nque about [4]. Dectons of n-dmensonal data can be authozed upon n paallel tomahawks n a 2-dmensonal aplane and connected by staght sectons. As demonstated out n the scholaly woks [5], numeous stateges have been pescbed to offe compehenson of multvaate nfomaton utlzng engagng ceaton stateges. Compaable facltate plots, as a staghtfowad howeve capable geometcal hgh-dmensonal nfomaton ceaton stategy and symbolzes N-dmensonal data n a 2-dmensonal zone wth factual metculousness. Vsual goupng, pvot eodeng and pont of vew concentatng ae nomal appoaches to educe jumbles unnng n paallel fts. Dasgupta et al. [6] suggested one n vew of sceen-space measuements to pck the tomahawks stuctue by enhancng sets up of tomahawks. Huh et al. gven a elated egon between two neaby tomahawks as opposed to the popotonal tetoy n egula PCP paallel tomahawks. Addtonally, the shapes havng a few measuable popety assocatng data consdes on close-by tomahawks ae potayed n atstc woks [7] too. Zhou et al. [8] changed ove the staght-lne sdes nto shapes to modeate up the obvous chaos n gouped ceaton. They addtonally utlzed the splattng stuctue [2] to ecognze gathengs and lessen notceable weckage. Wth the go fo avetng ove-plottng and ensung thckness data, Ka Lun Chung and We Zhuo [2] outlned two vsual explanatoy appaatuses: decson outlnes and espects dagams, to lessen vsual chaos unnng n paallel blends. The decson outlne s a bushng gadget whch helps clents undelnes the egons pcked. The espects dagam mastemnds gathengs and offe communcatons fo clents to see moe about the assocatons between gathengs. Julan Hench et al [9] planned BCluste Audence that consoldates heat maps and paallel blends plots to fnd nfomaton desgns. The BCluste Audence contans numeous ntellgent elements, fo example, pvot obtanng, go shadng, o cusng that dmnsh data fllng n notceable dagam. Matej Novotny and Helwg Hause [8] oganzed the data pont of vew nto anomales, and at that pont nclned and focused on the vewpont n mastemnded paallel dectons to modeate up the fllng ssues. Xaou Yuan et al [7] spead components unnng n paallel blends to unon paallel facltates and scatteplot clmbng, whch dmnshed nfomaton swamng. Clents can eode the blends by pullng the tomahawks n the nteface fo bette obvous watche. No past wok, to the best of ou nsght, has made two DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

3 exta tomahawks by utlzng the denstes fo the paallel sot out ceaton. We analyzed the data desgn to begn wth keepng n mnd the end goal to pocue the of SD and RD, and afte that pctued the data hubs. These data styles dmnshed nfomaton ove-lappng and populatng. 5Ws stength paallel dectons have consdeably deceased nfomaton fllng fo Bg Data analyss and ceaton Ws Paallel Coodnate Model Man suggestons of ths model epesent as follows Dmensonal Model: As the name (5Ws Dmensons) suggest that When data occu, Whee data fom, What data contans, Why data occu and Who eceve data. 5Ws dmensons llustated wth followng axes to defne data n vaous condtons. ) T { t1, t2, t3,..., t } epesents when data occued ) P { p1, p2, p3,..., p } epesents whee data fom ) X { x1, x2, x3,..., x } epesents what data contan v) Y { y1, y2, y3,..., y } epesents how data tansfe fom one to othe v) Z { z1, z2, z3,..., z } epesents why data occu v) Q { q1, q2, q3,..., q } epesents who eceved. Model of the densty wth attbutes access as shown n fg 2. Fg.2. Paallel coodnate 5Ws densty model pesentaton. Ths s example pesentaton of densty levels wth data pattens p=, x=, y=, z= and q=. Then the mappng functons to epesent these paametes wth followng functon.e. f (, t p, x, y, z, q ). Whee t T { } s suffcent tme seal fo each nfomaton occuence. p P{ } symbolzes whee the nfomaton came fom, such as Twtte, Face book o Sende. x X{ } symbolzes what the data content was, such as lke, dslke o attack. y Y{ } epesents how the nfomaton was moved, such as by the Intenet, by phone o by emal. z Z{ } symbolzes why the data occued, such as shang photos, fndng fends o speadng a vus. q Q{ } symbolzes who obtaned the nfomaton, such as fend, bank account o eceve Sendng & Recevng: Sende densty (SD) used to calculate and measue of sende wth data pattens fo accuate o patcula attbute p=, x=, y=, z= n tme t, then sendng densty and ecevng densty (RD) as follows concuently: F(,,, ) SD(,,, ) 100% F Ths equaton pesents 5Ws sende data patten wth dffeent data attbutes, then ecevng densty s as follows: F(,,, ) RD(,,, ) 100% F Ths equaton pesents 5Ws eceve data patten wth dffeent data attbutes. DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

4 2.3. Paallel Data Vsualzaton Axes: make two exta tomahawks by poposng two denstes SD ( ) and RD ( ), whch each have an ncentve fo evey nfomaton desgn, fo paallel facltate epesentaton so as to enhance exactness n paallel oganze epesentaton. The estmatons of SD ( ) and RD ( ) n both tomahawks speak to the nfomaton steam desgns appeaed as poly-lnes among the 5Ws measuements. Ths deceases nfomaton jumblng n the chat snce one subset has just a sngle polylne. 5Ws thckness paallel tomahawks, consoldated wth the n sequental ode tomahawks and numecal tomahawks, have gven moe scentfc technques fo Bg Data epesentaton. No nfomaton desgns have been lost amd the nvestgaton and pecepton handle Re-ode wth Clusteng: The 5Ws densty paallel dectons wth e-equestng and gouped pespectves gve vsual stuctues and examples to outlne the cozy connecton between the tomahawks n a ealstc fomat. It obvously exhbts Bg Data desgns fo vaous datasets, dvese themes and dstnctve nfomaton sots n pecepton. III. SYSTEM DESIGN & IMPLEMENTATION In ths secton, we dscuss about ou poposed appoach smlaty measue pocedue wth dffeent attbutes and elatons and ndexes n pactcal examples. Consde the pocedue dscussed n secton 3, to epesent data wth mult-vew cluste based on smlaty measue. To desgn ths mplementaton then followng modules ae equed to defne effcent attbute elatons Related wok: Based on tem and document fequency n uploaded data sets, we calculate Eucldan dstance between wods and smlaty between documents wth attbute elatons. Descpton of dffeent paametes used n ou appoach shown n table 1. TABLE 1. Dffeent paamete descpton. Paamete Descpton n,m,c,k,d Numbe of documents, tems, classes, clustes, and document facto d =1 S = {d1,..., dn},s Set of documents n cluste D= d d S Composte vecto of documents D= d d S Composte documents fo cluste C D/ n Centod vecto documents Centod vecto documents fo cluste C D / n Ths table summazes basc used notatons used n ths pape to calculate dffeent data epesentatons. Eucldan dstance evaluaton fo dffeent documents as follows: Dst (d, dj) = d - dj Dstance wth cluste fomaton n dffeent attbutes n elatonshps as follows: k mn d C 1 d S Based on vecto pesentaton fom oveall data sets wth smla data objects as follows: t Sm(d, dj) = cos(d, dj) = d d Cosne smlaty fo dffeent attbutes shown n above equaton pesentaton fo k-means wth Eucldan dstance, smlaty magntudes ae man dffeence between Eucldan dstance and k-means dstance fom oveall data sets. Some of the eseaches defne moe sequental clusteng data pesentaton to access dffeent attbutes n cosne smlaty attbute pesentaton Smlaty Measue: Cosne smlaty fo dffeent attbutes consdes sm equaton n above secton wthout changng the meanng n dffeent attbutes. t Sm(d, dj) = cos(d-0, dj-0) = (d-0) (dj-0) Whee o and 0 epesents vecto wth ogn pont n dffeent data pont evaluaton, the evaluate eques 0 as one and only bluepnt. The lkeness between two documents d and dj s establshed w..t. the angle between the two factos when lookng fom the ognal souce. To buld a new dea of lkeness, t s possble to use moe than just one efeals facto. We may have a moe pecse evaluaton of how nea o 2 j DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

5 emote a couple of factos ae, f we look at them fom many dffeent vewponts. A assumpton of goup subscptons has been ceated befoe to the evaluate. The two thngs to be calculated must be n the same goup, whle the ponts fom whee to detemne ths statstc must be outsde of the goup. We efe to ths as offe the Mult-Vewpont based Smlaty. Smlaty measue fo dffeent documents pesentaton wth attbutes as follows: 1 t MVS(d, d j d, dj S= ( d dh)( d j dh) n n \ The lkeness between two factos d and dj nsde cluste S, consdeed fom a facto dh outsde ths goup, s equal to the tem of the cosne of the poston between d and dj lookng fom dh and the Eucldean anges fom dh to these two ponts Implementaton: In ths secton, we pesent the mplementaton pocedue of ou poposed appoach to defne effcent data pesentaton n dffeent dmensons wth effectve smlaty measues between data objects. Mult vew pont smlaty measue fo stuctue documents as follows: 1 t t t t MVS(d, d j d, dj S)= ( dd j dd h dd j h dd h h) n n d S\ S d h S S t 1 t 1 t = dd j d dh d dh 1, dh 1 n n n n dh Compae two smla documents wth attbutes elatons fo all documents, MVS (d, d j ) and MVS (d, d l ), papes dj s moe smla to papes d than the othe papes dl s, f and only f. Implementaton pocedue of the MVS wth smla attbutes as show n followng fgue 3. dh Fg.3. Pocedue MVS (mult vew Smlaty) n smlaty matx. Fg. 3. Fst of all, the extenal blend w..t. each categoy s detemned. Then, fo each ow a of A, = 1,.., n, f the happy couple of ecods d and dj, j = 1,..., n ae n the same categoy, aj s measued as n ange 10, Fg. 3. Othewse, dj s beleved to be n d s categoy, and aj s measued as n ange 12. Ths s the smlaty matx pocedue to defne dffeent attbutes n data sets. DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

6 Fg.4. Mult-vew data vsualzaton fo dffeent eal tme data sets wth dffeent chaactestcs Cluste Label Data Pesentaton: Two genune epot datasets ae utlzed as cases n ths legtmacy test. The fst s eutes7, a subset of the enowned gatheng, Reutes Dstbuton 1.0, of Reute's newswe atcles1. Reutes s one of the most boadly utlzed test gatheng fo content ode. In ou legtmacy test, we chose 2,500 achves fom the bggest 7 classfcatons: "acq", "ough", "ntgue", "acque", "cash fx", "shp" and "exchange" to shape eutes7. A poton of the achves may show up n moe than one classfcaton. The second dataset s k1b, an accumulaton of 2,340 webste pages fom the Yahoo! subject pogesson, ncludng 6 ponts: "wellbeng", amusement", "bandsh", "legslatve ssues","tech" and "busness". It was made fom a past evew n data ecovey called WebAce [6], and s pesently accessble wth the CLUTO toolbox [9]. The two datasets wee pepocessed by stop-wod expulson and stemmng. In addton, we evacuated wods that show up n unde two epots o ove 99.5% of the aggegate numbe of achves. At last, the epots wee weghted by TF-IDF and standadzed to unt vectos. The full attbutes of eutes7 and k1b ae dsplayed n Fg. 4. The valdty test has shown the pospectve benefts of the new mult-vewpont centeed lkeness evaluate n compason to the cosne evaluate. IV. COMPUTATIONAL EVALUATION In ths secton, we dscuss pefomance evaluaton pocedue egadng data vsualzaton fo both paallel coodnate densty model and ou popose appoach Smlaty Measue Centeed wth Mult Vew Pont fo dffeent data objectves. Fo that we ae takng dffeent softwae paametes lke JDK 1.8 and Net Beans 8.0 fo use nteface constucton to upload data sets and pocess data sets usng dffeent paametes n elable data steam evaluaton wth espect to data pesentaton n dffeent fomats Data sets collecton: The nfomaton copoa that we used fo tests nclude of 20 conventonal papes datasets. Besdes eutes7 and k1b, whch have been descbed n nfomaton pevously, we nvolved anothe 18 wtten text selectons so that the study of the clusteng technques s moe thoough and compehensve. Just lke k1b, these datasets ae offeed togethe wth CLUTO by the toolkt s wtes [19]. They had been used fo tal examnng ove the documents, and the esouce and esouce had also been descbed n nfomaton. Table 2 summazes the featues. The copoa exstng a vaety of dmenson, vaety of sessons and categoy stablty. They wee all pepocessed by conventonal technques, such as stop-wod emoval, asng, elmnaton of too unusual as well as too egula tems wth nomalzaton. TABLE-2: Sample document datasets n dffeent fomats (Collected fom vaous data avalable lnks). DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

7 4.2. Expemental Results: To llustate how well MVSCs s capable of dong, we compae them wth fve othe clusteng technques on the 20 datasets n Desk 2. To sum up, the seven clusteng technques ae: MVSC-IR: MVSC usng equements opeate IR 5Ws Densty Model : MVSC usng equements opeate IV K-means: conventonal k-means wth Eucldean dstance Spkmeans: ounded k-means wth CS gaphcs: CLUTO s chat technque wth CS gaphej: CLUTO s chat wth polonged Jaccad MMC: Spectal Mn-Max Cut ctea [13] Ou MVSC-IR and MVSC-IV applcatons ae mplemented n Coffee. The contollng aspect α n IR s always set at 0.3 dung the tests. Nothng unless thee ae othe optons calculatons ae ensued to dscove woldwde deal, and evey one of them ae ntoducton subodnate. Hencefoth, fo evey stategy, we pefomed goupng a couple tmes wth haphazadly nstated values, and pcked the best tal as fa as the elatng taget wok esteem. In evey one of the analyses, each tal compsed of 10 tals. In addton, the outcome detaled hee on each dataset by a specfc bunchng technque s the nomal of 10 tals. Fgue 6 show the accuacy of ou poposed appoach wth dffeent data sets evaluaton pocedue on text oented documents wth feasble paametes wth values shown n table-3. Fg.5. Accuacy of dffeent data sets n dffeent data vsualzaton. TABLE -3: Accuacy values Documents 5Ws Model SMCMV Tme effcency esults ae plotted wth followng values show n table 4. The pesented of pefomance evaluaton of ou poposed appoach wth tadtonal appoach shown n fgue 6 wth espect to tme effcency n eal tme data set pocessng. TABLE -4: Tme effcency values Documents SMCMV 5Ws Model DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

8 Fg.6. Tme effcency values of both poposed and tadtonal appoaches wth dffeent data sets. Fnally, we descbe and conclude SMCMV appoach gves bette and effcency esults than 5Ws densty model fo dffeent types of documents elated to dffeent types of documents. V. CONCLUSION In ths pape, we pesent to dscuss about data vsualzaton wth dffeent data sets, and also dscuss about paallel coodnates data vsualzaton n data epesentaton based on topc, type of data and data sets. Fo smlaty measue of dffeent data objects n data sets, fo that we popose to develop novel method.e. Smlaty Measue Centeed wth Mult Vewpont (SMCMV) wth cosne smlaty fo dffeent text, mage, vdeo documents. We also compae data vsualzaton dffeence between paallel coodnate densty model pesentaton and ou poposed appoach n both theoetcal and pactcal fo lage data documents. The man key pont of ou poposed appoach to defne data sets n mult vew data epesentaton. Futhe mpovement of ou poposed appoach s to defne data documents n paallel pocessng usng advanced machne leanng appoaches wth eal tme data sets. REFERENCES [1] Jnson Zhang, Wen Bo Wang, Bg Data Densty Analytcs usng Paallel Coodnate Vsualzaton, 2014 IEEE 17th Intenatonal Confeence on Computatonal Scence and Engneeng. [2] Pngdom, Intenet 2012 n numbes, posted on Jan 16, 2013, [3] J. Sanyal, S. zhang, J. Dye, A. Mece, P. Ambun, and R.J. Moohead, Noodles: A Tool fo Vsualzaton on Numecal Weathe Model Ensemble Uncetanty, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 16, no 6, pp , Nov/Dec [4] S. Hadak, H.J Schulz, and H. Schumann, In Stu Exploaton of Lage Dynamc Netwoks, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 17, no 12, pp , Dec [5] Y.S. Wang, C. Wang, T.Y. Lee, and K.L. Ma, Featue-Pesevng Volume Data Reducton and Focus+Context Vsualzaton, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 17, no 2, pp , Feb 2011 [6] S. Afzal, R. Macejewsk, Y. Jang, N. Elmqvst, and D.S. Ebet, Spatal Text Vsualzaton Usng Automatc Typogaphc Maps, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 18, no 12, pp , Dec [7] A.H. Meghdad, and P. Ian, Inteactve Exploaton of Suvellance Vdeo though Acton Shot Summazaton and Tajectoy Vsualzaton, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 19, no 12, pp , Dec 2013 [8] E. Lamboay, S. Wumln, and M. Goss, Data Steamng n Telepesence Envonments, IEEE Tansactons on Vsualzaton and Compute Gaphcs, vol. 11, no 6, pp , Nov/Dec 2005 [9] L. Sh, Q. Lao, X. Sun, Y. Chen and C. Ln, Scalable Netwok Taffc Vsualzaton Usng Compessed Gaphs, In Poc IEEE Intenatonal Confeence on Bg Data (IEEE BgData 2013), pp , Oct 2013 [10] W. Cu, Y. Wu, S. Lu, F. We, M.X. Zhou, and H. QU, Context- Pesevng, Dynamc Wod Cloud Vsualzaton, IEEE Compute Gaphcs and Applcatons, vol. 30, no 6, pp , Nov/Dec 2010 [11] J. Zhang and M.L Huang, 5Ws Model fo Bg Data Analyss and Vsualzaton, In Poc th IEEE Intenatonal Confeence on Computatonal Scence and Engneeng (CSE), pp , Dec 2013 [12] A. Shav, H. Shav, M. Tavallaee, and A.A. Ghoban, Towad developng a systematc appoach to geneate benchmak datasets fo ntuson detecton, Computes & Secuty, vol. 31, no. 3, pp ,May 2012 [13] W.S. Seol, H.W. Jeong, B. Lee and H.Y. Youn, Reducton of Assocaton Rules fo Bg Data Sets n Socally-Awae Computng, In Poc th IEEE Intenatonal Confeence on Computatonal Scence and Engneeng (CSE), pp , Dec 2013 [14] Z. Wang, W. Xao, B. Ge, and H. Xu, ADaw: A novel socal netwok vsualzaton tool wth attbute-based layout and colong, In Poc IEEE Intenatonal Confeence on Bg Data (IEEE BgData 2013), pp , Oct 2013 [15] J. Zhang and M.L. Huang, Densty appoach: a new model fo BgData analyss and vsualzaton, Concuency and Computaton: Pactce and Expeence. publsh onlne July 2014, DOI: /cpe.3337 [16] Z. Wang, J. Zhou, W. Chen, C. Chen, J. Lao and R. Macejewsk, A Novel Vsual analytcs Appoach fo Clusteng Lage-Scale Socal Data, In Poc IEEE Intenatonal Confeence on Bg Data (IEEE BgData 2013), pp , Oct [17] Duc Thang Nguyen, Lhu Chen, Clusteng wth Mult-Vewpont based Smlaty Measue, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. XX, NO. YY, [18] Y. Zhao and G. Kayps, Empcal and theoetcal compasons of selected cteon functons fo document clusteng, Mach. Lean., vol. 55, no. 3, pp , Jun DOI: /jet/2017/v93/ Vol 9 No 3 Jun-Jul

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