Research on Data Mining Model of Intelligent Transportation Based on Granular Computing

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1 pp Research on Data Mnng Model of Intellgent Transportaton Based on Granular Computng Xao-Lan Xe and Xao-Feng Gu * 1 College of Informaton Scence and Engneerng, Guln Unversty Of Technology, Guln,Guangx Zhuang Autonomous Regon, Chna, Guln Unversty of Technology, GuangX key Laboratory of Embedded Technology and Intellgent Informaton Processng, Chna, College of mechancal and control engneerng, Guln Unversty Of Technology, Guln, Guangx Zhuang Autonomous Regon, Chna, gxf19995@163.com Abstract Through the analyss of the relatonshp between current ntellgence transportaton and large data, after the theory of granular computng concepts nvolved n prelmnary studes, For the current data mnng of ntellgent transportaton,we presents a Data Mnng Model Of Intellgent Transportaton Based on Granular Computng. Utlzng granular computng n data mnng theory advantages, through constructng a new data mnng model to solve the tlarge-scale, complexty, uncertanty and ambguty problems of massve data from ntellgent transportaton. Keyword: Intellgent Transportaton; Granular Computng; Data Mnng ;Data granulated 1. Introducton Wth the process of modernzaton s development, ntellgent transportaton has accumulated a mass of complex data. As a result, ntellgent transportaton rases new requrements on data mnng. By ths way we am to ncrease the effcency of transportaton management and transportaton servce, through processng the data and comprehensve applcaton. The servce of level of transportaton system s expected to mprove, and the ncdence of transportaton accdent s also hoped to reduce. So far, Chna ntellgent transportaton system has stepped nto the stage of practcal explotaton and applcaton from the exploraton stage. In term of applcaton area, the ntellgent transportaton n Chna s manly appled to nformatonzaton of transportaton, urban road transportaton control servce, and urban publc transportaton. The basc thought of granular computng, whch s the world vew and methodology as the new emergng object n data mnng, s to use the granular n the process of solvng the problem. The granule s the group, class or clusterng of element. The descrpton of granular structure and the granular computng can be descrbed from two dmensons. The former one focuses on the formaton, expresson and explanaton of the granule. The latter one s to apply the granular to the process of solvng problems. Granular computng has already been one of the man methods of soft computng. More detals about the theory of granular computng wll be ntroduced n the followng. * Correspondng Author ISSN: IJSIA Copyrght c 016 SERSC

2 . The Basc Concept and Theory of Granular Computng.1. The Basc Theory of Granular Computng.1.1. Informaton Granule, Granular Layer, and Granular Structure: (1) Granule In dfferent granular computng models, the granule s the smple block of the most basc elements, whch delvers clearer meanngs under dfferent forms of the characterstcs and performance of data and nformaton. Defnton.1 Granule: Provded there s the relatonshp R of doman of dscourse between U and U that U P( U) U U G, one G s a granule, of whch R s some knd of relevant relaton. Defnton. Granularty: Provded there s the granulaton U G of doman of U and U, the granularty of G s d ( G ) card ( G ) G dx. The granularty s the measurng standard for the sze of the granule. A quantty of granules n dfferent granulartes form the granular layer. Generally speakng, the smaller the granular layer s, the clearer the descrpton to the problem wll be. () Granular Layer In most cases, granular layer s consttuted by nformaton granule measured by the granulatng standard. In the same layer, all granules share thesame nature. In dfferent layers, granules n hgher layers are coarser than the counterparts n the lower one. Thus, the lower layer would be preferred to analyze problems more specfcally, by whch the addton of parts of nature would delver more mcromesh dvsons. (3) Granular Structure The granular structure s the relaton structure conssted by the nterrelaton among dfferent granular layers, under the dfferent granulatng crterons. It s the combnaton of ether a sngle mult-layer structure, or multple layered structures. As a result, granular structure s able to analyze the system from multple perspectves, more vsualzed and deepgong..1..informaton Granularty and Granular Computng: (1) Informaton Granularty Informaton granularty s a structural process. Smply speakng, t s the process that a granular layer comes out from the settng granulatng crteron; s the creaton of the basc unt of granular computng, ncludng granule, granular vew, granular net and herarchcal structure. Usually we decompose the coarse granule to get the fne granule by the top-down granulatng method, or combne the fne granule to get the coarse granule through the down-top method. () Granular Computng Granular computng s usually used to solve the practcal problem based on the dfferent granules, granular layers and granular structures. The mutual transformaton of the same problem n the same layer or among dfferent layers helps us to rase the effcency to solve these complex problems. Based on the deep analyss and study of granular computng theory, we dscover that most dscplne prncples are nterrelated. Meanwhle, granular computng and dscplne are mutual ndependent. Once the thought G 8 Copyrght c 016 SERSC

3 of granular computng s appled to solvng the practcal problem, granular computng would be lkely to be appled to any area. 3. A Data Mnng Model of Intellgent Transportaton Based on Granular Computng Due to the uncertanty, ambguty, large-scale and complex of the massve transportaton data, for mnng analyss of ntegraton, pretreatment and subsequent across multple data centers of mass urban transport nformaton data, and unpredctable traffc system behavor, the task of urban traffc data mnng reaches the bottleneck, usng exstng data mnng algorthms s very dffcult. So we propose the data mnng model of ntellgent transportaton based on granular computng here. Data Base Data Mnng Model Of Intellgent Transportaton statc state Based on Granular Computng Data Warehouse dynamc Web Informaton Source Dynamc Data Source Data Preprocessng Dscretzaton of COntnuous Attrbutes Incomplete Data Processng Constructon of Granular Layer Granular STructure Intalzng Data granulated Hugeness Granulaton of parallel felds To Overcome The Hugeness Mult granularty / cross granularty mechansm Guaranteed Delay Hgh Speed Parallel / ncremental granular structure updatng Depth study of mult granularty computng Automatc Generaton Of Rule Dversty Selecton of data sources and data ntegraton Reduce uncertanty Determne the support and confdence of the rule Mnng type selecton Gven Requrements Custom Mnng Rule Interpretaton Rule mnng Gven Condton Requrements Custom Condton Mnng Applcaton Applcaton Custom Mnng Fgure 1. Data Mnng Model of Intellgent Transportaton based on Granular Computng Copyrght c 016 SERSC 83

4 (1) Data Preprocessng When we conduct ntellgent transportaton data excavaton, after screenng data prelmnarly from a data source, t must frst s data preprocessng. For the most data mnng algorthms, n order to contnue to use, the contnuous attrbute data must be dscreted. Dscretzaton of contnuous attrbutes s an mportant step n data mnng study. The dscrete method of granule calculaton can be seen as splt and merge,based on the nterval merged gudelnes gven, under the premse of the mnmzed nformaton loss, through addng to breakponts to splt adjacent ntervals or removng breakponts to merged adjacent ntervals. We wll see the all average values of two adjacent and not same property values form sample set as a canddate breakponts, two adjacent canddate breakponts consstng of an nterval granule. To defne evaluaton crtera of merged range, at the same tme, mergng to acheve the most approprate neghborng nterval of the evaluaton crtera value. thus the condton attrbute range nto a number of the approprate nterval granule, then usng the selected nterval granule to dscrete for condtonal attrbutes. For example, accordng to the value x 1, x... x n of condtons property named a, we can get the ntal range granules that the amount s n when the average value s obtaned by sortng from small to large of each attrbute, The frst nterval granule referred to as x The second nterval granule referred to as x By party of reasonng, we can get x x 1 1, x x, 1 x3 x n1 x n, xn When the ntal nterval granule s the same as the total number of samples, that s, all the attrbute values are dfferent, each nterval contans a property value. But there are more of the same attrbute values n actual data, n order to get less ntal nterval granule, we can use some algorthms to fnd all non redundant breakponts, at the same tme, the two adjacent break ponts are formed nto a sequence of ntervals, thus we can obtan a smaller number of canddates for a set of breakponts. After dscretzaton of data attrbutes, there may be some part of the object attrbute value s not unque or unknown, ncomplete data processng s to be performed at ths tme. The defnton of ncomplete nformaton system s gven here: Defnton.1 In nformaton system S ( U, A, V, f ), f there s at least one property a A and an object xu make the value s default, t s called ncomplete nformaton system. In ncomplete data processng, you can merge all attrbute values wth the same object and ncrease the number of a column for the same class of objects to make some small objects nto a large object. Each object s a collecton of small objects, and consttute an nformaton granule, ths process reduces the number of objects n the orgnal decson system, the length of the decson table system s compressed, t accord wth the thought of granular computng. 84 Copyrght c 016 SERSC

5 () Data Granulated After ncomplete data processng, accordng to the data of the nature s not clear, or smlar set of functonal aggregaton obtaned an object, at ths tme, the data sze s sutable for partcle sze. The same granular layer can be dvded nto the same granular nformaton, at the same tme to ntalze the granular structure. The free swtchng of granularty takes nto account the decomposton and combnaton of multple granularty and the rapd constructon of the correspondng solutons. Constructon of the correspondng granular layer and the structure of each partcle layer on some specfc problems are needed to consder multple granularty layers of nformaton, and through usng the "cross granularty" mechansm to solve the problem. From the whole process, t can be found that the orgnal data whether has the approprate sze to provde gudance for confrmng whether need to adjust and how to adjust collecton of the generaton data. Selecton of data sources and data ntegraton s to confrm whch data to solve the problem may be helpful, and whch s not related to the topc. We accordng to the way that s the same attrbute values to granulate, there s a nformaton system S ( U, AT C { d}), t s recorded as GS ( U, GC GD) after nformaton granulated, for a decson table wth value N, nformaton system after granulated s recorded as GS ( U, GC GD, N). (3) Rule Mnng In ths model, mnng rules are called classfcaton assocaton rules. The attrbutes of nformaton system reduced a lot after attrbute reducton. A seres of decson rules are obtaned by the AND operaton of each nformaton granule after reducton and decson attrbute granule. We can get the rules of reducton after puttng the decson rule granule whose support degree and certanty factor (CF) reach the requrements nto the rule base. Users gve the support degree and CF threshold of rules and dg out the rules that meet the requrements of support and CF, we call t the rules automatcally. It can also defne the decson requrements or condtons requrements accordng to user requrements, and dg out the smplest rules that satsfy these requrements, we call them custom decson mnng and custom condton mnng. Fnally explan the mnng rules wth the orgnal meanng of the attrbute. (4) Applcaton The functon we desgn n ths module s very lttle, because decson applcaton requres people's subjectve regulaton and decson. Therefore t requres users determne whether t can be used n data decson, rules that not be used for decson must be abandoned; only those rules wth hgh value or revealng the nternal rules can be used for decson. The rules after the explanaton are derved from data mnng or the results of the concdence of data. As ths module nvolves more subjectve regulatory judgments and does not nvolve the specfc content of granular computng, we wll not elaborate. 4. Concluson Intellgent transportaton s an mportant part of smart ctes and the basc necesstes of lfe, and socety and t plays a crucal role for the development of cty. Granular computng, as the research object of artfcal ntellgence machne learnng, data mnng and other popular areas, s rarely appled to the development and constructon of Intellgent transportaton. So on ths bass, ths paper proposed the ntellgent transportaton data mnng model based on granular computng. Ths model prelmnary shapng and exploratons are gven a detaled explanaton, and the thought and methods of analyss and solvng problem of granular computng are ntegrated nto ths model, the unque advantages that usng t to reduce uncertanty and complexty problem can solve the uncertanty, fuzzness and complexty n ntellgent transportaton data mnng. Copyrght c 016 SERSC 85

6 Because ths paper only makes a prelmnary study on the model, so we don t make too much statement about some algorthms of data mnng nvolved n ths paper. We wll contnue to explore and research for future practcal applcatons. Acknowledgments Ths research work was supported by the Natonal Natural Scence Foundaton of Chna (Grant No ), GuangX key Laboratory of Embedded Technology and Intellgent Informaton Processng. References [1] Z.-Luo and W.-Jun, Granular Computng Appled to Data-mnng of Tunnel Informaton[C], 009 Frst Internatonal Workshop on Educaton Technology and Computer Scence, (009), pp [] Y. Yao, Apror model of granular computng[c], LNCS, Transactons on rough sets, (004). [3] T. Y. Ln, Data Mnng and Machne Orented Modelng: A Granular Computng Approach [J], Journal of Appled Intellgence, vol. 13, no., (000), pp [4] Y. Y. Yao and J. T. Yao, Inducton of Classfcaton Rules by Granular Computng Fuzzy Systems[C], 00, FUZZ, IEEE 0. In: Proc. ofthe 00 IEEE Intl. Conf. on., no. 1, (00), pp [5] Y.Y. Ln, Granular Computng: Examples, Intutons and Modelng[C], In: the Proceedngs of 005 IEEE Internatonal Conference on Granular computng, Bejng Chna, (005) July 5-7, pp [6] Zhang L. and Zhang B., A quotent space approxmaton model of multre soluton sgnal analyss[j], Journal of Computer Scence and Technology, vol. 0, no. 1, (005), pp [7] W.-Deng and G.-Y. Wang and Y.-Wu, Summary of granular computng [J], Computer Scence, vol. 31, no. 10A, (004), pp [8] W.-Deng, G.-Y. Wang and Y.-Wu. An Improvement n Evolutonary Computaton for Granular Computng[J], Computer Scence, vol. 31, no. 10, (004), pp [9] T. Y. Ln, Granular computng: fuzzy logc and rough sets[c], Computng wth Words n Informaton/Intellgent Systems, PhyscaVerlag, (1999). [10] A. Skowrom, J. Stepanuk and J. F. Peter, Extractng Patterns Usng Informaton Granules [J], Bulletn of nternatonal rough set socety, vol. 5, no. 1/, (001), pp Authors Xao-Lan Xe, She got her PhD n Xdan Unversty, Shan X, Chna. She s a Professor n School of nformaton scence and engneerng, Guln Unversty of Technology. Areas of research nclude Cloud computng, Grd computng and Intellgent decson system. She s a commttee member and deputy secretary general of Cloud computng expert commttee of Chna communcaton socety. She s also a member of Chna computer socety CCF and IEEE CS. Xao-Feng Gu, He s Mr. canddate. Hs research nterests nclude Cloud computng, data mnng, etc. 作者姓名 职称 / 学位 单位 地址 手机 Emal 谢晓兰 教授 桂林理工大学 雁山校区 @qq.com 谷晓峰 硕士 桂林理工大学 雁山校区 gxf19995@163.com 86 Copyrght c 016 SERSC