Application of Improved Decision Tree Method based on Rough Set in Building Smart Medical Analysis CRM System

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1 , pp Applcaton of Improved Decson Tree Method based on Rough Set n Buldng Smart Medcal Analyss CRM System Hongsheng Xu *, Lan Wang and Wenl Gan Luoyang Normal Unversty Henan Luoyang, 4710, Chna * xhs_ls@sna.com Abstract Medcal Customer Relatonshp Management (CRM) s a knd of study method for the patent and potental patent carres on the exchange, tmely access to and convey nformaton, trackng to gve the necessary gudance. The purpose of communty hosptal CRM s the daly busness management and decson analyss of the hosptal wth the relatonshp between doctors and patents. Decson tree learnng s an nductve learnng algorthm based example. Rough set theory s used to process uncertan and mprecse nformaton. In ths paper, a decson tree algorthm based on rough set s proposed, and the mproved decson tree algorthm based on rough classfcaton s better than the standard C4.5 algorthm n classfcaton accuracy and regresson rate by experment. Fnally, the mproved decson tree method s appled to the smart medcal analyss CRM system. The expermental results show that the method can mprove the management effcency of CRM. Keywords: Customer Relatonshp Management; Decson tree; Rough set; Smart medcal treatment; Attrbute reducton 1. Introducton Customer Relatonshp Management (CRM) s the customer relatonshp management. From the word meanng, refers to the relatonshp between the management and the enterprse CRM customers. CRM s customer value and ts relatonshp to a busness strategy selecton and management, CRM requrements to the customer as the center of the busness phlosophy and corporate culture to support effectve marketng, sales and servce process. If the enterprse has the correct leadershp, strategy and enterprse culture, CRM applcaton wll realze the effectve customer relatonshp management for the enterprse. State Councl n 009 health care reform deas proposed "establsh and perfect the basc medcal and health system coverng both urban and rural resdents, long-term goals for the masses to provde a safe, effectve, convenent and affordable medcal and health servces", a few years later, medcal reform gradually on the rght track, t not only affects the wth the publc nature of the state owned medcal unts, but also brng great mpetus to the development to the communty health care. To solve the problem of the common people, the doctor, the expensve, the role of communty health care s not neglgble. Due to the lack of relevant systems and the lack of funds, resultng n the medcal communty s not optmstc realty, such as poor medcal hardware, communty medcal nsttutons practtoners personnel vacances, the masses lack of trust on the communty health care, nether do the "Wumart", and doesn't do t "cheap", communty medcal care and basc medcal nsurance of dsconnecton, communty medcal nsttutons lack of effectve supervson and so on. However, Chna should take all measures to develop * Correspondng Author ISSN: IJSH Copyrght c 016 SERSC

2 communty medcal treatment to resolve the problem of "hard medcal treatment and expensve medcal treatment". One way s to apply computer nformaton technology to communty health care, so that t can meet the needs of more resdents n the lmted funds. Wth the reform of medcal system, communty hosptal s becomng the mportant barrer for the health of communty resdents, and becomes an mportant part of communty lfe. It s responsble for the treatment of common dseases of communty resdents, and the preventon of common dseases. The relatonshp between the communty hosptal and the resdents t serves s gradually deepenng [1]. The goal of customer relatonshp management system n communty hosptal s to make the relatonshp between doctors and patents, and manage the other tangble assets of the hosptal. System reflects the patents for the center for advanced busness phlosophy, through the establshment of all resdents n the communty wth the health fle database, on the health status of resdents of subdvson, mplementaton of every resdent n the personalzed medcal servce plan, mplementaton of the resdents to focus on dsease trackng survey and dsease preventon programs to develop, and ultmately acheve a wn-wn stuaton between the hosptal and the resdents. Decson tree learnng s an example based nductve learnng algorthm, J.R.Qunlan has made a detaled theoretcal descrpton of the. Decson tree learnng focuses on the classfcaton rules of decson tree representaton from a set of non order and rregular nstances.. It uses a top-down recursve way, n the nternal nodes of the decson tree attrbutes. Accordng to the dfferent property values to determne from the juncton, a branch of the downward n the leaf nodes of the decson tree, we obtan the followng conclusons. So from the root to the leaf node of a path corresponds to a conjunctve rule, the whole decson tree correspondng a set of dsjunctve expresson rules. Rough set theory s a mathematcal theory of the analyss of data from the Poland mathematcan Pawlak Z. n 198, whch s manly used to deal wth uncertan and mprecse nformaton.. Its characterstcs s does not need to be pre gven some attrbutes and characterstcs of the number of descrpton, but drectly from the gven problem descrpton set to fnd the nherent law of the problem []. The basc dea s closer to the realty. Now has been part of the research on rough set theory s appled n the decson tree, such as the frst of the data sets of attrbute reducton, and decson tree s constructed based on the core, the method to construct the decson tree by usng attrbute reducton to remove the nose and redundant attrbutes. Resoluton s defned, resoluton s used as the crteron to construct decson tree. Usng the rough set attrbute classfcaton rough degree as the splttng attrbute standards, accordng to the attrbute classfcaton rough degree by constructng decson tree, also n ths paper proposed usng varable precson rough set nose removal method. The standard of dvdng the attrbute s used n the lterature, and the suppresson factor s ntroduced to avod the decson tree. When the restranng factor s less than a certan value, the decson tree s no longer. Proposed n the lterature usng core attrbutes and dentfy the matrx to select the largest contrbuton to the classfcaton of attrbutes. In the lterature, the dependence of the attrbute of decson attrbutes on the condtonal attrbute s proposed as the heurstc nformaton to select attrbutes. C4.5 algorthm for decson tree learnng problems and t s from the ID3 algorthm to expand and come. These problems nclude: determne the depth of decson tree growth; to deal wth contnuous valued attrbutes, selecton of an approprate attrbute selecton measure; processng attrbute value ncomplete tranng data. To deal wth the consderaton of varous attrbutes; mprove the computatonal effcency. A mproved decson tree communty medcal analyss type CRM system research based on rough set, from large amounts of data quckly mnng user feelng n rules and ts applcaton to the analyss of smart medcal CRM system has a very mportant theoretcal value and 5 Copyrght c 016 SERSC

3 practcal sgnfcance s proposed, based on the rough set and decson tree theory of these two methods.. Method of Improved Decson Tree C4.5 based on Rough Set Decson tree (decson tree) s used for the man technology of classfcaton and predcton, decson tree learnng s by example based nductve learnng algorthm, through the example of a group of out of order, no rules to nfer the decson tree classfcaton rule. Decson tree algorthm s a method of approachng the value of the dscrete functon. It s a typcal classfcaton method, frst of all data processng, usng nductve algorthm to generate readable rules and decson tree, and then use decson to analyze new data. Essentally, a decson tree s a process of classfyng data through a seres of rules [3]. The basc algorthm of decson tree nducton s the greedy algorthm, whch s based on top-down recursve way by constructng a decson tree. The basc strateges of the algorthm are as follows: (1)The tree begns wth a sngle node representng the tranng sample; ()If the samples are n the same class, the node becomes the leaves, and t s used to mark; (3)The otherwse, the algorthm uses nformaton gan based on entropy measurement as heurstc nformaton, select a sample classfcaton attrbutes can best be called. The attrbute becomes the test or decson attrbute of the node. (4)The every known test attrbute value s to create a branch, and then dvde the sample. (5)The algorthm uses the same process, the formaton of the sample decson tree recursvely on each partton. Once a property appears on a node, t s not necessary to consder t n any descendant of the node. (6)It can dvde step only f one of the followng condtons set up stop. Rough set theory as a computatonal ntellgence scence research, whether t s n theory or n practce has made great progress, and t has been successfully used n artfcal ntellgence, knowledge and data dscovery, pattern recognton and classfcaton, fault detecton and t. Defnton 1: Informaton system S { U, Q, V, f }, ncludng U: a fnte set of objects; Q: a fnte set of attrbute Q C D, C: condton attrbutes subset, D: decson attrbute subset; V: range of attrbutes, Vp attrbute range; f : U A V s a total functon, makng for each X U, q A, there are (, ) f X q V q. In the nformaton system S { U, Q, V, f }, X U s a subset of the global, ndvdual attrbute subset P Q, then: X Lower approxmaton set: P X { Y U / P : Y X } X Upper approxmaton set: P X { Y U / P : Y X } X Boundary regon: ( ) B n d X P X P X P The collecton PX of X U those elements that are bound to be classfed P, U s based on the subset of attrbutes, and all of the collecton of elements X that can be ncluded n the collecton, that s, the maxmum defned set wthn t. A collecton of B n d ( X ) those elements X U that s nether classfed nor classfed P n the U X upper [4]. The larger B n d ( X ) the boundares of the collecton and t are the P smaller the degree of the determnaton. In the rough set theory, knowledge s consdered as the ablty to classfy objects of real or abstract objects. A knowledge base of U can be understood as a relatonal system, Copyrght c 016 SERSC 53

4 where U s the doman, and R s the equvalent relatonshp of U. Decson table nformaton system and decson table, he s a knd of specal and mportant knowledge expresson system, s also a knd of specal nformaton table, t sad when certan condtons are met decson (behavor, operaton, control) should be how to t [5]. It s a two-dmensonal table, each row descrbng an object, each column descrbng an attrbute of the object. Attrbute s dvded nto condtonal attrbute and decson attrbute. The object of the doman s classfed nto decson makng wth dfferent decson attrbutes accordng to dfferent condtonal attrbutes, as s shown by Fgure 1. Fgure 1. Rough Set Classfcaton Chart Defnton : the nformaton system S { U, Q, V, f }, U s for the object of the fnte set X X X m, s dvded nto a fnte sample set of examples, makng,,,, 1 X U, X m j,, j 1,,, m, X 1, X X j U Q C D, C s Attrbute set, whch sets the attrbute set for the decson attrbute set. p P C Attrbute, then rough classfcaton s defned as: m C S D ( p, C, D ) K ( P, D ) * ( X ) P 1 m The classfcaton accuracy ( X ) of each attrbute set p obtaned by the attrbute s 1 p demonstrated. ( X ) Values ndcate that the attrbutes to decson attrbute sample set of p classfcaton accuracy, larger values, that value ( X ) s greater, that c a rd ( P X ) c a rd ( B n d ( X )) c a rd ( P X ) p s caused by uncertan factors less, the effect of classfcaton, B n d p ( X ) the better; on the contrary, that the attrbute set of classfcaton results s not obvous, namely the classfcaton uncertanty [6]. Thus, ( X ) t s demonstrated that the P attrbute s a measure of the classfcaton accuracy of all sets. The decson tree uses the gan nformaton metrc to select test attrbutes at each node of the tree.. Ths metrc s called an attrbute choce metrc or a splt measure of the pros and cons.. Select the attrbutes of the hghest nformaton gan (or maxmum entropy compresson) as the test attrbute of the current node. Ths property makes the amount of nformaton needed s for the dvson of the sample classfcaton mnmum, and reflect the dvson of mnmum random or "mpurty". Ths nformaton theory makes the desred number of the desred test mnmum for an object classfcaton, and s ensured to fnd a smple tree. Defnton 3: let S be a collecton of s data samples. If the attrbute of the class label has a dfferent value, the m m s defned as dfferent C (=1,..., m). Let S be the sample m 1 p p (1) 54 Copyrght c 016 SERSC

5 number of class C. The desred nformaton for a gven sample classfcaton s gven by the followng: I m S, S,, S = p log p 1 m () 1 Among them, PI s the probablty of any sample belongng to C, and estmated by S / S. Note that the logarthm to the base, because nformaton n bnary code. Usually C4.5 algorthm s the most sutable for the followng problems: The example s the "attrbute value par sad: nstance s to use a fxed set of attrbutes and ther values to descrbe. C4.5 algorthm not only can deal wth dscrete values, also allows the processng doman for real property. Defnton 4: Generatng total-1 segmentaton ponts s n the sequence of values. The value of I (0<<total) s set to V= (Ac+A (+1) C) /, whch can be dvded nto two subsets of the data set on the node [7]. 1 1 c ( d )(1 x ), f x 1 d K ( x) E (3) 0, f x 1 The objectve functon wth dscrete output value: functon C4.5 algorthm can learn more than two dscrete output values. But t can't learn the functon that has real number value output. It may requre dsjunctve descrpton (dsjunctve descrpton): the decson tree C4.5 algorthm to generate naturally represents the dsjuncton expresson. Defnton 5: let P. R, when P s ndependent, and Ind (P) =Ind (R), then sad R s a reducton of P, denoted as Red. R R all the non relatonal composton of the collecton known as the nuclear Core. Push and prove: Core= Red. In the nternal nodes of the decson tree for comparson of attrbute values, and accordng to the dfferent attrbute value judgment, n the leaf nodes of decson tree, we obtan the followng conclusons from the node, a branch of the downward; the whole process s repeated wth a new node to the root of the subtree. For example, an example of classfcaton s from the root node of the tree to test the nodes represent attrbutes, then along a branch of the attrbute value movng down, repeat the process untl the leaf node s reached, the nstance belongs to class. Defnton 6: let s be a contan s a sample data set, class attrbute can take M dfferent values, correspondng to m dfferent categores of C, I epslon {1,,3... M}. If S s a sample number n the class C, then the amount of nformaton requred for a gven data object s the follow equaton (4). E [ B ( t ), B ( t )] H 1 C ( t ) (4) E [ B ( t ) ] Where H s the Hurst exponent and C s the correlaton coeffcent. In rough set theory, "knowledge" understandng s the ablty of classfcaton, the dvson of the data, the avalable set representaton, for example, assumng a gven data set u and equvalence relaton set P, f P dvdes u, t s called knowledge. Knowledge reducton s refers to n the nsurance to the classfcaton or decson ablty of a knowledge base nvarant condtons, delete the rrelevant or unmportant knowledge, whch can smplfy the judgment rules, to mprove the effcency of decson-makng. In practcal applcaton, the decson table s usually used to descrbe each object n the doman. Usng the decson tree to carry out the classfcaton manly contans two steps. Copyrght c 016 SERSC 55

6 Step (1): to construct a decson tree model usng the tranng data set. Ths process s actually a process of machne learnng from the knowledge acqured from the data. Step (): to classfy the unknown nput data by usng the decson tree model. On the nput record, the attrbute values of the records from the root node are sequentally tested untl a leaf node s reached, thereby fndng the class of the record. The key of the two processes s the constructon of decson tree. Based on the above analyss, we use degree of rough classfcaton as the standard splttng attrbute CSD (p,c,d), s able to reflect the attrbute classfcaton accuracy s guaranteed to construct the decson tree classfcaton,, but also take nto account the dependence of condton attrbute and decson attrbute of the decson tree classfcaton more effectve. 3. Smart Medcal Analyss CRM System The medcal feld of CRM s a research method for the patent and potental patent carres on the exchange, tmely access to and convey nformaton, trackng to gve the necessary gudance. From the perspectve of a non-proft organzaton, medcal nsttutons should be to an nsurance or unnsured patent wth qualty of medcal servce [8]. In order to acheve a balance n terms of proftablty, managng the relatonshp wth the patent to hosptal s partcularly crtcal, lock those payments for Medcare patent, and ncrease ther loyalty, n order to obtan more profts to cover the unnsured patent. Wth ncreasng competton n medcal nsttutons, medcal nsttutons have begun to focus on how to mprove the medcal nsttutons of the core compettveness of the problem, began to make varous efforts, try to provde dfferentated and personalzed servce for the patent. Communty medcal patent relatonshp management s a real "take the customer as the center" of the management system, the nvestment s a knd of effectve management phlosophy s not only for patents to provde perfect personalzed servce and cultvate loyal qualty patents, more can promote communty health comprehensve compettveness, brng the long-term economc beneft s best hosptal profts rsng breakthrough. In the cost reducton, the customer relatonshp management makes the sales and marketng process automaton, greatly reducng the sales expenses and marketng expenses. And, because the customer relatonshp management to enterprses and customers have hghly nteractve, help the enterprse to realze customer more accurate postonng, so that enterprses retan old customers, gan new customers the cost decreased sgnfcantly. In hand, ncrease ncome, due to the process of customer relatonshp management n the hands of the large number of customer nformaton can be through data mnng technques to dscover customer potental demand, cross sellng can brng addtonal new sources of ncome. And, due to the use of customer relatonshp management, can more closely relatonshp wth customers, ncrease the number and frequency of orders, reduce customer loss. Customer churn analyss and modelng s a new applcaton of applcaton data mnng technology. In short, the predcton model s a pattern of dscovery from the database, and s used to forecast the future [9]. Customer churn predcton model of smple sad s from the customer data warehouse n extracton of a certan amount of tranng samples, after pretreatment of tranng set s formed, by usng data mnng methods, the formaton of predctve models, predcted by the model to the new sample classfcaton, predct whether a customer has the loss of possblty, as s shown by equaton (5). E a C ( a ( k ) R k ), j ( a C ( a ( k ) R k )), j (5) tr {( a C ( a ( k ) R k )) } 56 Copyrght c 016 SERSC

7 Where, E s that Customer relatonshp management of the relatonshp between the enterprse CRM to manage and customer, C(ak) s a busness strategy for choosng and managng a valued customer and ts relatonshp. CRM requres a customer centered busness phlosophy and corporate culture to support effectve marketng. Above we can make a smple understandng for e-commerce and CRM, we now the commercal software market, CRM trend can be descrbed s lke a duck to water, recent natonal polces tend to emphasze the n the small and medum-szed enterprse, n CRM users nsde the small and medum-szed enterprse user occupes a large part of the ponts, and CRM tself has a good flexblty. Usng nformaton technology to transform the enterprse management mode, establsh to offce automaton, fnancal management nformaton system mplementaton n Enterprse Resource Plannng (ERP), supply chan management (SCM), customer relatonshp management (CRM) as the target of an ntegrated management system, buld a web ste or through the ntermedary of the network to carry out nformaton exchange and the development of electronc commerce, realze the management nnovaton of enterprses and medcal and publc health. In 013, the state wll contnue to accelerate transformaton of the mode of economc development, adjust and optmze the ndustral structure, mprove the overall qualty of the ndustry, started the "Twelfth Fve Year Plan" natonal major scentfc and technologcal nfrastructure constructon, accelerate the promoton of major projects of strategc emergng ndustres, mplementaton of a number of hgh technology major projects, to accelerate the development of a major nformaton technology. The sgnfcance of optmzng customer value s through a seres of actvtes, so that we gradually become the mportant envronment n the value chan of the other party. Ths not only keeps the low cost of contnuous sales, but also enables us to control the value chan of the other party, so that we can get the maxmum proft [10]. Optmze product / servce n customer value chan space and poston ponts, optmze customer value frst step s to optmze our products n customer value chan space poston. From the secondary poston gradually to the man, key poston for replacement. n 1 x x f ( x ) k ( ) d n h h 1 (6) Communty hosptal customer relatonshp management system (HCRM) to the hosptal based on the doctor-patent relatonshp of daly affars management and decson analyss, n equaton (6) f(x) s manly for customers, x s follow-up servces, k s complants and other aspects of the data were collect and collate, to mprove the patent's degree of satsfacton and the loyalty. Customer relatonshp management s the process of the enterprse n the face of customer, from the judgment, choce, strve to develop and mantan the whole process to mplement. The frst country n development of CRM s the Unted States, domestc CRM started late, but shows a strong momentum of development, n foregn countres, computer technology has been used n the hosptal for more than 40 years of hstory, the Unted States s about n the early 1960s, and the earlest began hs research. In recent years, the hosptal nformaton system n Chna has great development. But the applcaton of CRM system n communty medcal system has just begun. Hosptal (Medcal Management System HMMS) based on the computer grd s the man force for hosptal management and operaton. After ten years of development, has begun to take shape and the paperless economc accountng automaton, offce treatment, medcal electronc fles, graphcs mage dgtalzaton, ntegrated nformaton network of prncple, to the unfcaton of the system standard drecton. Customer relatonshp management s usng modern technques, the customer, competton, brand, three elements of coordnated operaton and realze the optmzaton Copyrght c 016 SERSC 57

8 of the whole system, the goal s enhance the compettve ablty of the enterprses n the market and support long-term customer relatonshps, contnue to tap the new sales and servce opportuntes, so that enterprses and ultmately acheve sustaned growth n sales revenue, profts and shareholder value. 4. Smart Medcal Analyss CRM System based on Rough Set Improved Decson Tree Decson tree s a smlar to the flow chart of the tree structure, whch each nternal node sad test on an attrbute, each branch represents a test output, and each node represents classes or class dstrbutons, the topmost node of the tree s the root node. More explctly sad that the decson tree s accordng to the root node to leaf nodes of the order of examples classfed. Among them, each node represents an attrbute and each branch represents that t s connected to the node n the attrbute value. In the basc structure dagram of the decson tree, the mddle node s often expressed n rectangular nodes, and the leaf nodes are represented by ellpse, as s shown n Fgure. Fgure. Basc Structure Dagram of Decson Tree The C4.5 algorthm uses the top-down greedy search to traverse the possble decson tree space, whch can be descrbed as a hypothess from a hypothetcal space searchng for a fttng tranng sample [11]. The assumpton that the C4.5 algorthm searches s that the decson tree s possble. C4.5 algorthm to a from smple to complex hll-clmbng algorthm to traverse the hypothess space, startng from the empty tree, then gradually consder more complex assumptons to search to decson tree wth a correct classfcaton of the tranng data. Ths paper s to solve the tradtonal mnng algorthm effcency s not hgh, redundancy rules to be bg, the user only to them were nterested n a part of the rules and other ssues, rough set and tradtonal decson tree mnng algorthms are combned, n order to mprove the effcency and practcalty of decson tree mnng and medcal nsttutons n the communty analyss CRM system applcaton. In ths paper, we use rough classfcaton to construct decson tree. The decson tree based on rough classfcaton s the standard of attrbute classfcaton accuracy and condtonal attrbute and decson attrbute.. The greater the roughness of attrbute, the more the determnaton of the attrbute, and the dependence of the attrbute and decson attrbute. After a large number of examples of the analyss, t s n the process of splttng the attrbute; t s based on rough classfcaton decson tree algorthm for the selected attrbute classfcaton accuracy to better than C4.5 algorthm selecton wth the maxmum nformaton gan propertes. Rough set theory can analyze based on past a large amount of emprcal data to fnd these rules, rough set based decson support system n ths area makes up the shortage of 58 Copyrght c 016 SERSC

9 conventonal decson-makng methods, allow the decson objects exst some not too clear, less complete attrbute and after reasonng that almost certanly the concluson. Knowledge dscovery from the database, modern socety, wth the rapd development of nformaton ndustry, a large number of nformaton from the fnancal, medcal, scentfc research and other felds of nformaton s stored n the database. The data mnng process based on rough set ncludes data preprocessng, reducton (ncludng attrbute reducton and attrbute value reducton) and rule extracton. Frstly, the tranng set s classfed accordng to the attrbute and the category, and the classfcaton rules are generated accordng to the relatonshp between the subsets of the attrbute subsets and the approxmate and the lower approxmaton of the target attrbute subsets. In practce, the advantage of rough set knowledge reducton and other classfcaton technques are used to classfy ncomplete data [1]. Applcaton of rough set attrbute sgnfcance of tranng samples of 17 attrbutes by learnng to form a tranng sample of 1 attrbutes, based on usng C4.5 algorthm modelng, greatly mprove the effcency of learnng. Expermental results show that the model s robust and stable. Attrbute reducton: a notable feature of data mnng method based on rough set s that t has explct knowledge expresson form. Accordng to the defnton of nformaton system n rough set theory, the attrbute A s dvded nto C and decson attrbute D, so we can get the C Then D If accordng to the nformaton table. In theory, we can get a rule for each record n the nformaton system. But the rule obtaned by the nformaton table s more condtonal, the generalzaton ablty of the rule s weak and the applcaton s narrow. Defnton 7: let.u X be a collecton, R s an equvalence relatonshp defned on U. A: 1 f R (x) =U{Y epslon U / R:Y epslon x}, R (x) for X r approxmaton set; () f R (x) =U{Y epslon U / R:Y U-shaped; X = Ph}, R (x) for X r approxmaton set; (3) f R (x) = a (x) - R (x) s called R (x) as the set X the boundares of the domans. If the R s empty (X), called X for the set of set R s clear; on the other hand, call set X s about rough set R. Defnton 8: let R s a famly of equvalence relatons, and {r} R, f nd (R) = nd R- {R}, s called {r}, R, otherwse known as {r} R can be omtted. m c a r d ( p X ) c a r d ( P O S ( D )) P 1 k ( P, D ) c a r d ( U ) c a r d ( U ) (7) The algorthm of mprovng decson tree based on rough set s as follows: Algorthm: Generate_decson_tree generates a judgng tree from the gven rough tranng data. Input: tranng sample samples, represented by dscrete value attrbutes; the collecton of canddate attrbutes attrbute_jst. Output: a decson tree. Method: (1) Create node N; () the nodes for all data samples n a contnuous type descrpton attrbute specfc values and ascendng sort attrbute values the value sequence {A1c, Ac... Atotalc}. (3) Returns N as a leaf node to class C Tags: (4) attrbute_lst If s empty. Then (5) Returns N as the leaf node, markng the most common class of samples; (6) If attrbute_lst s empty, return N as the leaf node, and tag the most common class of Samples; (7) Calculate the roughness of each attrbute n attrbute_lst; (8) S n S1 S,,..., by the value of t splt (accordng to k t may be Sk, S S1, S, Sk,); Copyrght c 016 SERSC 59

10 (9) Select the best segmentaton pont from the total-1 segmentaton pont. For each splt pont data set, the C4.5 calculates ts nformaton gan rato, and selects the segmentaton pont to partton the data set. (10) If S s empty, and a leaf s added, the most common class of Samples s marked. Otherwse, add a node returned by _Tree Generate_Decson (S, attrbute_lsttest_attrbute). C4.5 can handle the dscrete descrpton attrbutes and also handle the contnuty descrpton attrbute. In selectng a node branchng attrbutes for dscrete attrbute descrpton, C4.5 and ID3 s the same, accordng to the number of the attrbute values of the parameters were calculated; for a contnuous descrpton of the propertes of AC, assumng that the data n a node set number of samples for total. The establshment of decson tree conssts of two stages: the frst stage, the stage of buldng. Select the tranng data set for learnng, export decson tree. Decson tree nducton of the basc algorthm s a greedy algorthm, t uses s top-down recursve dvde and conquer approach to construct decson tree, algorthm s outlned as follows. The second stage: the prunng stage. Testng decson tree wth test data set, f the establshed decson tree can not correctly answered [13]. We want to decson tree prunng algorthm to solve the over adaptaton problem data untl the establshment of a correct decson tree. Defnton 9: (equvalence relaton) desgn knowledge representaton system s = (U, a, V, f), f the attrbute set P A, called P not resolved between nd (P) s the equvalence relaton on u, whch nd (P) = {(x, y) epslon U * u. Epslon P and f (x, a) = f (y, a)}. The set of all the equvalence classes derved from Sx(f) s denoted as P / U, whch consttutes a dvson of the doman and contans the equvalence class of X, denoted as p [x]: 1 1 S ( f ) C f, f C 0 (8) X Attrbute reducton, the basc theory of rough set and some expanson of the relevant theory of data analyss and reducton. So called knowledge reducton s to reduce the tme complexty of decson tree generaton, whch s based on the same ablty of keepng the classfcaton ablty of knowledge base on t. On a table nformaton data mnng decson rules, f the attrbute reducton can reduce decson tree mnng algorthm for the calculaton of the amount, can also reduce the redundancy of decson rules. Usng a reducton set RED from the decson system S= (U, A) to generate the rules of the process s qute drect. Intutvely, each reducton s used to form a decson rule for each object n the decson table, smply read from the approprate attrbute values from the table. In the form of equaton(9), f(a) s n the smlar logc language, f(b) wth the decson rule s expressed as x s the antecedent of the decson rule and the combnaton of the condtonal attrbute value. f ' x3 b f ( b ) f ( a ) (ln b ln a ) x f ' x ln 1 x (9) Decson rules mnng value reducton): value reducton rule acquston, reducton of decson rules s to elmnaton of decson rules n the necessary condton attrbute value that s to calculate each rule of nuclear and smplfed. After the reducton of attrbutes, the redundant values are elmnated. In the algorthm of decson tree model mnng, the attrbute of the rule concluson s reduced, and the decson rule s reduced. Usng a communty medcal nsttutons database smulaton experment s carred out, n practce on the bass to fnd the best soluton; n the optmzaton mproved the expermental results were analyzed, the algorthms or methods mproved better [14]. Of analytcal CRM functon desgn, focusng on from the target patents, patents wth potental, the opportunty for patents, patents, referral and management of patents wth sx functon modules were functonal dvson, n analyss module n the applcaton of mproved decson tree based on rough set mnng algorthm of potental patents, a 60 Copyrght c 016 SERSC

11 opportunty patents and management wth data for extractng the decson, analyss, to provde bass for decson makng for managers. Defnton 10: a varable X, t may have a varety of values, namely x1, X,..., xn, the probablty that each one s P1, P,..., Pn, then the entropy of X s defned as: t N 1 x ( t 1) ( t ) ( ) ( ) 1 b t x t s N t 3 0 N t N t 1 For the classfcaton system, the category C s a varable, and ts possble value s C, C1,...,Cn, whle the probablty of each category s P (C1), P (C),..., P (Cn), therefore n s the total number of categores. The end user of the system s dvded nto two categores: a class s user communty hosptal, communty hosptal department wll use the system records and call the communty resdents and health related nformaton servces for the communty resdents, and thus the health of resdents for scentfc and effectve for management and help. The other s communty resdents. Use the system to obtan the effectve data about the health status of oneself, and partcpate n the plan and the health related actvty arrangement. 5. Experments and Analyss Ths system s dvded nto customer management, customer servce management, marketng management, statstcal analyss and other modules. The frst step of the system s data preprocessng: the part of the nput data processng, ncludng the data mssng value, the attrbute dscretzaton and generalzaton. The actual storage of data n the database s affected by the factors such as human or physcal, and there are some nterference factors such as nose data, vacancy data and nconsstent data. So t s necessary to pre process the data n the database before data analyss and mnng, and provdes a flexble and convenent data abstracton platform for applcaton development. Theorem 1: let a property V take a dfferent value {a1, A, A,..., av}, the use of attrbute A can be dvded nto S collecton V sub set {S1, S,... And Sv}, ncludng SJ contans a set s of attrbute a n AJ value of sample data, f attrbute a was selected as the test attrbute (for the sample set dvson), let SIJ subset SJ belong to C of the sample set, usng propertes of dvdng a current sample collecton of nformaton entropy. Selected from the communty medcal nsttutons n the database part of the record and nterpret data, choose the mportant 6 effects explan the property of the concluson of condton attrbute set was composed, a decson attrbute, 158 object consttute tranng sample set. The decson tree s constructed by usng C4.5 and mproved decson tree algorthm based on rough classfcaton, and the two value dscretzaton of sample set s carred out before constructng decson tree. The process of buldng a decson tree s as follows. Step1: prunng (prunng) method, the man purpose s to remove the nose or abnormal data, make the decson tree algorthm has better generalzaton ablty. Prunng often usng a statstcal metrc, branches cut off the most unrelable, leadng to rapd classfcaton, ablty to rase the ndependent test data to carry on the card to make a classfcaton tree. Accordng to the mplementaton of the prunng tme s dvded nto two methods: pre prunng method and post prunng method. Step: after every record n the attrbute reducton of decson table can be used as a rule, but whch contans a large number of redundant nformaton, namely n the reducton of nformaton system, and not every record every attrbute values are for nformaton system and decson rules extracton work for a must for attrbute reducton results (10) Copyrght c 016 SERSC 61

12 contnue to smplfy. The redundant nformaton n the decson table after attrbute reducton s the attrbute value reducton. Actually, attrbute value reducton s further reducton, as s shown by equaton (11). q, j= + 1, p ( h) j lm u, j= -1, h 0, j 0. h 0 (11) Where q shows the mportance of attrbute mportance of decson tree, p s the bgger, h shows that the correlaton degree of attrbute set and decson attrbute u s hgher. When the attrbute set has only one attrbute. Step3: After the decson tree s constructed, the classfcaton rules can be extracted drectly from the decson tree, and the rule s expressed n the form of IF-THEN. Create a rule for each path from the root to the leaf node. Along the path of each attrbute value on the form before rule (part IF) a conjuncton. The leaf nodes contan classes of projectons that form the rule (THEN part). As the followng dagram s a decson tree that has been generated, the follow equaton (1): Pr f q z c a 1 q p / N (1) Where n s the number of nstances, f=e/n for the observed error rate (whch e n nstances classfcaton error number) and Q s the true error rate and C for the confdence (C4.5 algorthm of an nput parameter. The default value s 0.5), Z correspond to the confdence degree C standard devaton, ts value can be set accordng to the C value by lookng up table of normal dstrbuton s obtaned. Through ths formula, a confdence lmt of the true error rate Q can be calculated. Ths experment envronment s: the hardware envronment: CPU G, P43.8G memory, software envronment: Wndows7 flagshp verson, the desgn of the CRM system based on the Struts framework for the JEE platform. The expermental data s the data of the dsease of a communty medcal nsttuton after data preprocessng, the data set contans 5866 samples, each sample has 9 attrbutes, and the target s classfed nto 4.The experment s dvded nto two steps, frst step test when alternatve to generate bnary decson tree of decson tree classfcaton performance nfluence; second step test when payng attenton to small classes (ISPASS=) of decson tree classfcaton performance. The experment makes use of 0 tmes cross valdaton to evaluate the classfcaton effect. The results were 1 and respectvely. Table 1. Comparson of Classfcaton Results when Decson Tree Generated Classfcaton method StandardC4.5 Improved decson tree by Rough Set Category ISPASS= The mnmum sample number for the stop splt Precson Recall Node number ISPASS= Precson Recall Node number Copyrght c 016 SERSC

13 Selecton of ISPASS= class classfcaton accuracy hgher resoluton to stop the mnmum sample number ranges 80, set C1 s more than or equal to 0.8, C s more than or equal to 1.6, C3 =.5 for the second step expermental. Results are shown n Table shows: Classfcaton method Table. ISPASS= Class Classfcaton Results Concern ISPASS= ISPASS=1 Node number Precson Recall Precson Recall StandardC Improved decson tree by Rough Set In Table 1 and Table can be seen when flexble applcaton based on rough set mproved C4.5 algorthm to generate non equlbrum data for the bnary decson tree, not only ISPASS= small class classfcaton accuracy and regresson rate mprove and ISPASS=1 large class of accuracy of classfcaton and regresson rate also ncreased slghtly and decson tree complexty has a sgnfcant declne. Thus, n does not change the dstrbuton of samples s flexbly based on rough set mproved decson tree algorthm for mbalanced data set of decson tree generaton for a communty medcal nsttutons dsease patent data has a better classfcaton results. From the expermental results, t can be seen that based on rough classfcaton of the mproved decson tree algorthm n terms of the number of generated rules to more than the standard C4.5 algorthm, the algorthm for constructng decson tree s relatvely complex, but based on rough classfcaton algorthm of decson tree C4.5 algorthm wth an average accuracy of 6 percentage ponts hgher than. The expermental results are tested repeatedly and the stablty of the decson tree algorthm based on rough set s mproved. 6. Summary Customer relatonshp management s to customers as the center of busness strategy. It uses nformaton technology means, to restructure the enterprse work flow, so that enterprses and customers better communcaton, to acheve customer proftablty s maxmzed. Decson tree s a data mnng n a very effectve classfcaton method, classfcaton, predcton, rule extracton n sometmes nterested to some assocaton rules. Therefore, the mproved algorthm has become the hot research. Rough set s proposed by Z. Pawlak n the early 1980s, a for dealng wth uncertan and vague knowledge of the mathematcal tools, the basc dea s n the premse of keepng the ablty of classfcaton, through the reducton of knowledge, derved concept classfcaton rules, sutable for to fnd hdden n the data, potentally useful rules, fnd out the relatonshps and characterstcs n ts nternal data, has been wdely used n knowledge acquston, decson analyss, machne learnng and other felds. In ths paper, we frstly propose an mproved decson tree algorthm based on rough set, whch s based on the attrbute dvson, consderng both the attrbute classfcaton accuracy and the dependence of the condtonal attrbute and the decson attrbute.. The analyss of the large number of examples proves that the mproved decson tree algorthm based on the rough classfcaton algorthm s better than the standard C4.5 algorthm n the classfcaton accuracy of the classfcaton accuracy. Fnally, the decson tree based on rough set mproved mnng applcaton to the analyss of CRM system analyss management functon module, the whole data mnng process s dvded nto three steps: Copyrght c 016 SERSC 63

14 data preprocessng, attrbute reducton, decson rule mnng. Through the system can effectvely fnd, mantan and retan patents, mnng new patents, provde personalzed servce for patents, so as to realze the communty medcal nsttutons n the lmted captal and technology support ssued to patents wth better servce and realze the proft maxmzaton objectve, and provde a scentfc bass for decson-makng n the management, mprovng the ntellgent management level of the medcal communty. Acknowledgments Ths paper s supported by Scentfc and technologcal projects of Henan Provnce n Chna ( ), and also s supported by the scence and technology research major project of Henan provnce Educaton Department (13B50155) and Henan Provnce basc and fronter technology research project ( ). References [1] S. Tsumoto, Automated dscovery of postve and negatve know ledge n clncal databases, IEEE Engneerng n Medcne and Bology, (000), pp [] A. Kusak, J. A. Kern and K. H. Kernstm, Autonomous Decson-Makng A Data Mnng Approach, IEEE Transactons on Informaton Technology n Bomedcne, vol. 4, no. 4, (000), pp [3] H. Xu and R. Zhang, Semantc Annotaton of Ontology by Usng Rough Concept Lattce Isomorphc Model, Internatonal Journal of Hybrd Informaton Technology, vol. 8, no., (015), pp [4] W. H. Sang and Y. K. Jae, A New Decson Tree Algorthm Based on Rough Set Theory, Internatonal Journal of Innovatve Computng Informaton and Control, vol. 4, no. 10, (008), pp [5] K. D. Suprya and P. R. Krshna, Clusterng web Transactons Usng Rough Approxmaton, Fuzzy Sets and Systems, vol. 148, (004), pp [6] W. H. Sang and Y. K. Jae, Rough Set-based Decson Tree usng the Core Attrbutes Concept, Second Internatonal Conference on Innovatve Computng, Informaton and Control, Japan: IEEE, (007). [7] M. Yaha, R. Mahmodr and N. Sulmmann, Rough neural expert systerm, Expert system wth Applcaton, vol. 18, (00), pp [8] Y. Fang, An Approach to Evaluatng the Effectveness of Customer Relatonshp Management wth Interval Grey Lngustc Varables, JDCTA, vol. 7, no., (013), pp [9] G. Alvatore, M. Bentto and S. Roman, Rough set theory for mult crtera decson analyss, European Journal of Operatonal Research, vol. 19, (001), pp [10] H. Hong and J. Chun, The Performance of Customer Relatonshp Management System: antecedents and consequences, JCIT, vol. 8, no. 1, (013), pp [11] M. Sonajhara and J. Rajn, Rough Set Based Decson Tree Model for Classfcaton, 5th Internatonal Conference on data warehousng and knowledge dscovery, DEXA Socety, (003); Prague, Czech Republc. [1] A. A. Estaj, M. R. Hooshmandasl and B. Davvaz, Rough set theory appled to lattce theory, Inf. Sc. vol. 00, (01), pp [13] A. A. Bakar and A. Arshad, Rough Set and Decson Tree Model for Determnng Scholarshp Award Qualfcaton, RNIS, vol. 1, (013), pp [14] J. We, Rough Set based Approach to Selecton of Node, Internatonal Journal of Computatonal Cognton, vol. 1, no., (003), pp Authors Hongsheng Xu, he was born on December 8, Educatonal background: master, Henan Unversty, Kafeng, Chna, 007; Major feld of study: data mnng, Knowledge dscovery, artfcal ntellgence, Customer Relatonshp Management. 64 Copyrght c 016 SERSC

15 Lan Wang Professor, she was born on Nov 3,1967. Educatonal background: master, Northwestern Polytechncal Unversty,Xan, Chna, 007; Major feld of study: Customer Relatonshp Management, Decson tree, Rough set, data mnng, artfcal ntellgence. Copyrght c 016 SERSC 65

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