Mining Customer s Data for Vehicle Insurance Prediction System using k-means Clustering - An Application

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1 Iteratioal Joural of Computer Applicatios i Egieerig Scieces [VOL III, ISSUE IV, DECEMBER 2013] [ISSN: ] Miig Customer s Data for Vehicle Isurace Predictio System usig k-meas Clusterig - A Applicatio S. S. Thakur 1, ad J. K. Sig 2 1 MCKV Istitute of Egieerig/Departmet of Computer Sciece & Egieerig, Liluah, Howrah, Kolkata, West Begal, , Idia 2 Jadavpur Uiversity/Departmet of Computer Sciece & Egieerig,, Jadavpur, Kolkata, West Begal, , Idia subroto_thakur@yahoo.com, jk_koustav@yahoo.com Abstract Data miig or miig customer s data helps to discover the key characteristics from the customer s data, ad possibly use those characteristics for future predictio. The problem of selectig the best algorithm/parameter settig is a difficult oe. However k- Meas Clusterig is a algorithm helps to classify or to group the objects based o attributes/features ito k umber of groups. A good clusterig algorithm ideally should produce groups with distict o-overlappig boudaries, although a perfect separatio caot typically be achieved i practice. I this paper, a approach has bee made by collectig data samples from customers, ad the applyig clusterig o optimized data for Vehicle Isurace Predictio System. To determie which algorithm is good is a fuctio of the type of data available ad the particular purpose of aalysis. Keywords- Data Miig, Vehicle Isurace, k Meas Clusterig, Predictio, databases. I. INTRODUCTION Vehicle isurace (also kow as auto isurace, GAP isurace, car isurace, or motor isurace) is isurace purchased for cars, trucks, motorcycles, ad other road vehicles. The specific terms of vehicle isurace vary with legal regulatios i each regio. To a lesser degree vehicle isurace may additioally offer fiacial protectio agaist theft of the vehicle ad possibly damage to the vehicle, sustaied from thigs other tha traffic collisios. Car Isurace is madatory by law. Comprehesive car isurace protects your car from ay ma made or atural calamities like terrorist attacks, theft, riots, earth quake, cycloe, hurricae etc i additio to third party claims/damages. There are certai guidelies that should be followed by the Car Isurace buyers while choosig the policy. Car isurace [1] acts like a great fried at the time of crisis. There are certai geeral isurace compaies who also offer olie isurace service for the vehicle. The isurace compaies [1, 2] have tie-ups with leadig automobile maufacturers. They offer their customers istat auto quotes. Auto premium is determied by a umber of factors ad the amout of premium icreases with the rise i the price of the vehicle. The claims of the Auto Isurace i Idia ca be accidetal, theft claims or third party claims. Certai documets are required for claimig Auto Isurace i Idia, like duly siged claim form, RC copy of the vehicle, Drivig licese copy, FIR copy, Origial estimate ad policy copy. There are differet types of Auto Isurace i Idia: I. Private Car Isurace II. Two Wheeler Isurace III. Commercial Vehicle Isurace The auto isurace geerally icludes: Loss or damage by accidet, fire, lightig, self igitio, exteral explosio, burglary, housebreakig or theft, malicious act. Liability for third party ijury/death, third party property ad liability to paid driver. O paymet of appropriate additioal premium, loss or damage to electrical/electroic accessories. The auto isurace does ot iclude: Cosequetial loss, depreciatio, mechaical ad electrical breakdow, failure or breakage Whe vehicle is used outside the geographical area War or uclear perils ad druke drivig. 148 P a g e

2 Thakur et al. This paper outlies the implemetatio of Vehicle Isurace Predictio system usig k-meas clusterig algorithm. II. PROPOSED APPROACH Applicatio of k- Meas Clusterig for predictio of Vehicle Isurace system emphasizes some key areas. These are as follows: Our approach for Predictio of Olie Vehicle Isurace system has bee dealt i sectio 2. Algorithm for Vehicle Isurace Predictio system usig k Meas Clusterig has bee dealt i sectio 3. Implemetatio Methodology for predictio usig k Meas Clusterig, its workig priciple ad Pseudocode of traditioal k- meas has bee dealt i sectio 4. Experimetal evaluatio ad Results has bee dealt i sectio 5. Desig issues ad future work has bee dealt i sectio 6. Figure 1 shows the block diagram of the complete system, i which the predictio to customers for purchasig Olie Isurace is explaied i detail. Fig 1. Block diagram of the complete system I this work, at first we collected data samples/ dataset (Table 1) by askig 4 questios, to owers/drivers of the cars, motorcycles, who parked their vehicles i the parkig, i differet Shoppig Complex amely City Cetre I Salt Lake, City Cetre II Rajarhat, Mai square Easter Metropolita Bye Pass ad South City Mall at Jadavpur, Kolkata. TABLE 1. DATA SET The dataset show i Table 1, cotais 4 attributes amely Vehicle Ower (Yes/No), Qualificatio, Age ad if Vehicle Ower are iterested i Olie Isurace (Yes/No). This dataset is very importat as the predictio i this work is cocered. III. ALGORITHM VEHICLE INSURANCE PREDICTION SYSTEM Step 1: Collect the data set Step 2: Creatio of data base from the collected data Step 3: Apply Optimizatio techique for Step 2, to remove icosistecy Step 4: Formatio of modified database Step 5: Apply k-meas algorithm for Clusterig of Selective Attributes o modified database a: Accept the umber of clusters to group data ito ad the dataset to cluster as iput values b: Iitialize the first K clusters - Take first k istaces or - Take Radom samplig of k elemets c: Calculate the arithmetic meas of each cluster formed i the dataset. d: K-meas assigs each record i the dataset to oly oe of the iitial clusters e: Each record is assiged to the earest cluster usig a measure of distace (e.g. Euclidea distace). f: K-meas re-assigs each record i the dataset to the most similar cluster ad re- 149 P a g e

3 Miig Customer s Data for Vehicle Isurace Predictio System usig k-meas Clusterig - A Applicatio calculates the arithmetic mea of all the clusters i the dataset. Step 6: Formatio of Cluster Set {Cluster1, Cluster 2,, Cluster } Step 7: Repeat Step 8 for i=1 to, where is the umber of clusters Step 8: Check If (Cluster(i) = Cluster(i+1)) The Group Cluster (i) ad Cluster (i+1) ito same category Step 9: Apply Predicate Logic to get the fial result IV. IMPLEMENTATION METHODOLOGY Vehicle Isurace Predictio System: We collected data samples/ dataset by askig 4 questios, to owers/drivers of the cars, who parked their vehicles i the parkig, i differet Shoppig Complex i Kolkata. We asked for 4 attribute amely whether they are vehicle ower or ot, their qualificatio, their age ad whether they will opt for Olie Isurace if such facility is available to them. Based o the data collected a database is created usig MySql with all the iformatio available. As per our observatio we foud that Vehicle owers who are females are bit hesitat i tellig their age. I those cases we marked our sample collectio data sheet ad i creatio of database all the samples are icluded. Later o we apply optimizatio to remove icosistet or icomplete data, ad its becomes our modified database which cotais 112 records, ad before optimizatio the dataset size was 176 samples for 4 wheelers. Similarly we had 102 records, before optimizatio the dataset size was 160 samples for 2 wheelers ad all the data are real data. The we apply k-meas algorithm for clusterig of selective attributes o modified database. The attributes are qualificatio ad age. I case of qualificatio we had data samples for Madhyamik, Higher Secodary, Graduate ad Post graduate. Similarly we divide the age ito 4 age groups amely Age>20 & Age<25, Age>=25 & Age<=30, Age>30 & Age<=45, ad Age>45 & Age<=65. Simply speakig k-meas is a algorithm to classify or to group your objects based o attributes/features ito K umber of group where K is positive iteger umber. The groupig is doe by miimizig the sum of squares of distaces betwee data ad the correspodig cluster cetroid. Thus, the purpose of k-meas clusterig [3, 4] is to classify the data. Example: Suppose we have 4 objects as your traiig data poit ad each object have 2 attributes. Each attribute represets coordiate of the object (Table 2). TABLE 2. DATA SET EXAMPLE Thus, we also kow beforehad that these objects belog to two groups of medicie (cluster 1 ad cluster 2). The problem ow is to determie which medicies belog to cluster 1 ad which medicies belog to the other cluster [5]. Each medicie represets oe poit with two compoets coordiate. Now we explai how we had applied k-meas clusterig to our database. Give a dataset of data poits x 1, x 2,, x such that each data poit is i R d, the problem of fidig the miimum variace clusterig of the dataset ito k clusters is that of fidig k poits {m j } (j=1, 2,, k) i R d such that is miimized, where d(x i, m j ) deotes the Euclidea distace betwee x i ad m j. The poits {m j } (j=1, 2,,k) are kow as cluster cetroids. The problem i Eq.(1) is to fid k cluster cetroids, such that the average squared Euclidea distace (mea squared error, MSE) betwee a data poit ad its earest cluster cetroid is miimized [5,6]. 1/ i=1 [mi j d 2 (x i m j )] (1) The k-meas algorithm provides a easy method to implemet approximate solutio to Eq.(1). The reasos for the popularity of k-meas are ease ad simplicity of implemetatio, scalability, speed of covergece ad adaptability to sparse data. The k-meas algorithm ca be thought of as a gradiet descet procedure, which begis at startig cluster cetroids, ad iteratively updates these cetroids to decrease the objective fuctio i Eq.(1). The k-meas always coverge to a local miimum. The particular local miimum foud depeds o the startig cluster cetroids. The problem of fidig the global miimum is NP-complete. The k- meas algorithm updates cluster cetroids till local miimum is foud. 1/ i=1 1 i=1 X i (2) Before the k-meas algorithm coverges, distace ad cetroid calculatios are doe while loops are executed a umber of times, say l, where the positive iteger l is kow as the umber of k-meas iteratios. 150 P a g e

4 Thakur et al. The precise value of l varies depedig o the iitial startig cluster cetroids eve o the same dataset. So the computatioal time complexity of the algorithm is O(kl), where is the total umber of objects i the dataset, k is the required umber of clusters we idetified ad l is the umber of iteratios, k, l [6]. I our work iitially we had take four clusters as show i Table 3 based o qualificatio e.g. Madhyamik, Higher Secodary, Graduate ad Post graduate ad later o reduced the same to two clusters oly Madhyamik ad Higher Secodary i cluster 1, Graduate ad Post graduate i two clusters (Table 4). It ca be observed from Table 5, as there are four clusters i which there are total 44 customers havig Age>20 & Age<25, 34 customers havig Age>=25 ad Age<= 30, 15 customers havig Age>30 ad Age<= 45 ad 9 customers havig Age>45 ad Age<= 65. TABLE 6: DATA WITH 2 CLUSTERS FOR MOTORCYCLES TABLE 3: DATA WITH 4 CLUSTERS FOR CARS It ca be observed from Table 3, as there are four clusters i which there are total 14 customers havig Age>20 & Age<25, 16 customers havig Age>=25 ad Age<= 30, 30 customers havig Age>30 ad Age<= 45 ad 52 customers havig Age>45 ad Age<= 65. TABLE 4: DATA WITH 2 CLUSTERS FOR CARS It ca be observed from Table 6, as there are two clusters i which there are total 78 customers havig Age>=20 ad Age<=30, ad total 24 customers havig Age>= 30, ad Age<= 65. The we check for aother attribute i.e. whether the customers will go for olie isurace or ot, which is a very importat attribute i our work. Fially we check the last attribute whether the customer I vehicle ower or ot which helps us for doig the predictio as show i Table 7, 8. V. EXPERIMENTAL EVALUATION & RESULTS There have bee some promisig results from applyig k-meas clusterig algorithm with the Euclidea distace measure, where the distace is computed by fidig the square of the distace betwee each scores, summig the squares ad fidig the square root of the sum [6]. TABLE 7: RESULTS OBTAINED FOR CARS It ca be observed from Table 4, as there are two clusters i which there are total 30 customers havig Age>=20 ad Age<=30, ad total 82 customers havig Age>30 ad Age<= 65. TABLE 5: DATA WITH 4 CLUSTERS FOR MOTORCYCLES TABLE 8: RESULTS OBTAINED FOR MOTORCYCLES 151 P a g e

5 Miig Customer s Data for Vehicle Isurace Predictio System usig k-meas Clusterig - A Applicatio Fig. 4 - Results with 4 clusters for Motorcycles The overall performace is evaluated by applyig determiistic model i Equatio-2 where the group assessmet i each of the cluster size is evaluated by summig the average of the idividual scores i each cluster. Fig. 5 - Results with 2 clusters for Motorcycles Fig. 2 - Results with 4 clusters for Cars Fig. 3 Results with 2 clusters for Cars Further from Fig. 2 we observed that umbers of customers with qualificatio graduate are domiatig oes, irrespective to age criteria. At the same time we observed that age is also a importat criteria that requires further attetio. From Fig. 3 we observed that, whe we reduce the umber of clusters from four to two clusters, we foud that i additio to qualificatio the customers havig Age>30 ad Age <=65 eeds special attetio. Further from Fig. 4 we observed that umbers of customers with qualificatio Madhyamik, Higher Secodary are domiatig oes, irrespective to age criteria. At the same time we observed that age is also a importat criterio that requires further attetio. From Fig. 5 we observed that, whe we reduce the umber of clusters from four to two clusters, we foud that i additio to qualificatio the customers havig Age>=20 ad Age<=30 eeds special attetio. The results obtaied as show i Table 7 idicates that more tha 75% customers with qualificatio Graduate ad Post Graduate are iterested i Olie Isurace. Additioally we observed that Graduates are higher i umbers as vehicle owership of 4 wheelers is cocered followed by postgraduates. Similarly results obtaied as show i Table 8 idicates that more tha 60% customers with qualificatio Graduate ad Post Graduate are iterested i Olie Isurace. Additioally we observed that Graduates are higher i umbers as vehicle owership of 2 wheelers is cocered followed by Higher Secodary as qualificatio.. Hece if olie Isurace System is made available i our coutry, ot just for payig the amout of isurace, but also differet isurace schemes provided by the compaies, it will beefit both the customers as well as Isurace compaies. As customers are cocered they will be havig a optio/choice to select Isurace for their vehicles at competitive prices. At the same time Isurace Compay ca tap the customers based o their qualificatio ad age ad ca make more beefits. This paper presets k-meas clusterig algorithm as a simple ad efficiet tool to do the predictio for 152 P a g e

6 Thakur et al. Customers, which eables the customer to purchase Isurace policies with may beefits available for their 4 wheelers i.e. cars, 2 wheelers i.e. motorcycles as show i Figure 2, 3 ad Figure 4, 5. Figure of merit measures (idices) such as the silhouette width or the homogeeity idex ca be used to evaluate the quality of separatio obtaied usig a clusterig algorithm [6]. The cocept of stability of a clusterig algorithm was cosidered i [5]. The idea behid this validatio approach is that a algorithm should be rewarded for cosistecy. VI. DISCUSSION AND CONCLUSION I this paper, we implemeted traditioal k-meas clusterig algorithm [5] ad Euclidea distace measure of similarity was chose to be used i the aalysis of the Isurace Predictio system. We demostrated our techique usig k - Meas clusterig algorithm. This model improved o some of the limitatios of the existig methods, such as model developed by [7] ad [8, 9]. Also the research work by [10, 11, 12] oly provides Data Miig framework for Studets academic performace. However, the results obtaied from customers data shows that more tha 75% customers whose qualificatio is either graduate ad post graduate has show iterest i Olie Vehicle Isurace system i case of 4 wheelers i.e. Cars, whereas customers with the same qualificatio is above 60% who has show iterest i Olie Vehicle Isurace system i case of 2 wheelers i.e. motorcycles. We predict ad suggest that isurace compay ca tap the customers of this qualificatio, ad also customers of age betwee 30 to 65 years for 4 wheelers, ad customers of the age betwee 20 ad 30 for 2 wheelers ad provide them the services, which will beefit both the customers ad also the isurace compaies, hece wi-wi situatio for both of them. ACKNOWLEDGMENT [1] What determies the price of my policy?. Isurace Iformatio Istitute Retrieved 11 May [2] Am I covered?. Accidet Compesatio Corporatio. Retrieved 23 December 2011 [3] Susmita Datta ad Somath Datta, Comparisos ad validatio of statistical clusterig techiques for microarray gee expressio data, Bioiformatics, vol. 19, pp , [4] Sharmir R. ad Shara R., Algorithmic approaches to clusterig gee expressio data, I curret Topics i Computatioal Molecular Biology MIT Press; pp , [5] Fahim A. M., Salem A. M., Torkey F. A. ad Ramada M. A., A efficiet ehaced k-meas clusterig algorithm, Joural of Zhejiag Uiversity Sciece A., pp , 2006 [6] N. V. Aad Kumar ad G. V. Uma, Improvig Academic Performace of Studets by Applyig Data Miig Techique, Europea Joural of Scietific Research, vol. 34(4), [7] Varapro P. et al., Usig Rough Set theory for Automatic Data Aalysis, 29th Cogress o Sciece ad Techology of Thailad, [8] Aderberg, M.R., Cluster Aalysis for Applicatios, Academic Press, New York, 1973, pp [9] J. O. Omolehi, A. O. Eikuomehi, R. G. Jimoh ad K. Rauf, Profile of cojugate gradiet method algorithm o the performace appraisal for a fuzzy system, Africa Joural of Mathematics ad Computer Sciece Research, vol. 2(3), pp , [10] Cherkassky, V. ad Mulier, F. Learig from Data: Cocepts, Theory, ad Methods. Wiley Itersciece, [11] Ha, J. ad Kamber, M. Data Miig: Cocepts ad Techiques. Morga Kaufma Publishers, Sa Fracisco, 2001 [12] Had, D. J., Maila, H., ad Smyth, P. Priciples of Data Miig. MIT Press, 2001 The authors are thakful to Mr. Reetam Nath, Mr. Mooj Modal, studets of Fial Year, CSE Deptt, of MCKV Istitute of Egieerig, Liluah for their ivolvemet i data collectio for the said Research work. The authors are also thakful to Prof. Puspe Lahiri ad Prof. Abhisek Saha, Assistat Professor i CSE Deptt, of MCKV Istitute of Egieerig, Liluah for his valuable suggestios for the implemetatio of the algorithm i the said research work. The authors are also thakful to Prof. Parasar Badyopadhyay, Pricipal, MCKVIE, Liluah for givig permissio to use the labs. for carryig out the research work. 153 P a g e REFERENCES