Market Segmentation of Inbound Business Tourists to Thailand by Binding of Unsupervised and Supervised Learning Techniques

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1 334 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY 204 Market Segmentaton of Inbound Busness Toursts to Thaland by Bndng of Unsupervsed and Supervsed Learnng Technques Anongnart Srvhok Department of Computer Scence, Faculty of Scence Kasetsart Unversty, Bangkok, Thaland Emal: Wrot Yotsawat Department of Computer Scence, Faculty of Scence Kasetsart Unversty, Bangkok, Thaland Emal: Abstract Market segmentaton s an mportant tool, for drvng an organzaton to acheve ts goals. Ths study proposes a market segmentaton technque wth the bndng of unsupervsed and supervsed learnng technques. The method ams to cluster nternatonal toursts who arrved n Thaland for busness proposes, and to classfy busness toursts by usng the products of an unsupervsed learnng technque as class labels. A Self-Organzng Map (SOM), K- Means and Herarchcal clusterng were appled to fnd the best qualty of segmentaton guded by the computaton of the Slhouette ndex. Segment labels were used to supervse the learnng part as class labels. Multlayer Perceptron (MLP), J48 decson tree, Decson Table, OneR and Naïve Bayes classfers were used to classfy the busness tourst data set, and the best performance technque was preferred. The expermental results desgnated that K-Means outperformed the other clusterng technques and provded fve dfferent segments. Moreover, the Naïve Bayes classfer gave the best performance among the other classfers based on the busness tourst varables. Thus, ths model can be used to predct the segment of new arrval busness toursts. Index Terms market segmentaton, toursm, K-Means, unsupervsed learnng, supervse learnng, Naïve Bayes I. INTRODUCTION Each year many nbound toursts travel to Thaland for busness purposes such as exhbtons, conferences and meetngs. The busness toursts are mportant because they may brng nvestment from ther country to Thaland. Even though we don t know ther type of busness, the quantty of nvestment, the proft or loss rate or when the busnesses wll operate, they produce an effect on demand and supply n Thaland. Moreover, n the future, these busness people are lkely to come back agan for other busness reasons such as followng up on ther busnesses and expanson growth. Consequently, assocated organzatons must have better polces and plannng n order to attract and mantan ths tourst market. They have to understand the characterstcs of the toursts. Market segmentaton technques can be used to produce ths knowledge for the organzaton. Past research focused on nbound tourst market segmentaton [] and proposed data mnng of toursts by usng two step clusterng and classfcaton. The research found that the K-Means technque gave hgher qualty nformaton than SOM and Fuzzy C-Means (FCM) for tourst parttonng based on nternatonal toursts to Thaland. Moreover, MLP can predct the characterstcs of new toursts as part of the producton from clusterng technque. The results of ths research ndcated that each tourst segment was dfferent n terms of economcs. However, the mean value of daly expendture of busness tourst was hgher than nonbusness toursts. These expenses were computed from the data on overall toursts provded by the Department of Toursm n the Mnstry of Toursm and Sports, Thaland [2]. In the current study, we focus on nbound toursts who come to Thaland for busness proposes. The prmary objectves of ths study are () to compare the performances of K-Means, SOM neural network and Herarchcal clusterng technques n order to segment busness toursts and (2) to compare the performance of classfers namely, Decson Tree, Decson Table, OneR, MLP and Naïve Bayes, n order to predct the segment of new busness toursts as part of the producton from the clusterng technque. The remander of ths paper s organzed as follows. Secton 2 provdes a lterature revew on market segmentaton. The related algorthms of unsupervsed and supervsed learnng and the measurements are presented n Secton 3. Secton 4 detals the expermental desgn and ts results. Fnally, the concluson and future work are n Secton 5. II. MARKET SEGMENTATION Market segmentaton s a methodologcal process of dvdng markets whch are comprsed of ndvduals nto do:0.4304/jsw

2 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY smaller groups wth homogenous characterstcs wthn each segment and heterogenety between segments, based on an dentfed set of attrbutes [3]. There are many technques for market segmentaton. However, the most mportant technque for dentfy segments s clusterng. Boone et al. [4] suggested that there are over 50 clusterng technques whch can be appled for market segmentaton. K-Means and Herarchcal clusterng (Ward method) are the most popular n market segmentaton as suggested by S. Dolncar [5]. Moreover, a SOM neural network can be appled for market segmentaton and several dfferent felds [6]. Some researchers proposed market segmentaton technques wth combned algorthms. R.J. Kuo et al. [7] proposed the ntegraton of SOM and K-Means for market segmentaton. They found that ther proposed technque provded slghtly better parttonng than the conventonal methods such as the ntegraton of herarchcal and K- Means or only SOM. However, no clusterng technques were sutable for all data. Some researchers have worked on toursm market segmentaton such as J. Z. Bloom [8] who proposed tourst market segmentaton usng a SOM neural network for parttonng the tourst data set. Also, a BP neural network was used for predctng the segmentaton of new toursts as part of already exstng segments. M. Najm et al. [9] proposed the segmentaton of nbound toursm market n Iran wth a concentraton on culture. In the frst stage, they used agglomeratve herarchcal clusterng to extract the core segments. Subsequently, common sense segmentaton was performed to obtan the fnal segments. Moreover, J. Chang [0] proposed the segmentaton of toursts who travelled n the Ruka trbal area, Tawan. The research used Herarchcal clusterng (Ward method) to partton the data and used K-Means to obtan the fnal segments. Some toursm market segmentaton research has focused on a specfc market such as J. Km et al. [] who consdered senor Australan toursts who were older than 49 years. They used SOM to partton the data set nto four segments. E. M. Garca et al. [2] focused on toursts n Span who flew on low-cost arlnes to Grona Arport, Span. They used two step clusterng whch was proposed by M. Wedel et al. [3]. In the frst step, Herarchcal clusterng (Ward method) was performed to partton the data, then K-Means was appled to obtan the fnal segments. H. L. T. Trang [4] proposed an nbound toursm market segmentaton n Thaland. The researcher focused on the toursts of the Andaman cluster (Phuket, Phang-Nga and Krab), Thaland. Herarchcal clusterng (ward method) was used to partton the data set. Then, K-Means was appled to obtan the fnal segments. Moreover, J. G. Brda et al. [5] used the ntegraton of SOM and K-Means, proposed by J. Vesanto et al. [6], to partton toursm data. They focused on three dfferent Chrstmas markets n Northern Italy. In the current study, we focused on the segmentaton of nbound busness toursts to Thaland. We appled SOM, K-Means and Herarchcal clusterng n order to partton the data set. The best qualty technque was selected. Then, classfcaton technques were appled to predct the segments of new toursts as part of the producton from clusterng technque. The best performance classfer was selected. III. RELATED ALGORITHMS A. Unsupervsed Learnng In ths study, we obtan the segments of the busness tourst market by usng the clusterng method. K-Means clusterng s a smple partton method. It s the most popular method n cluster analyss [7] and was proposed by James MacQueen n 967. The basc concept of K- Means s the parttonng of n data ponts nto k clusters by the nearest mean. Thus, the mportant nput requrement of K-Means s the number of clusters. Some mportant problems of K-Means are the optmum number of clusters and the outlers of nput data. To avod the problems of K-Means, we fnd the optmum number of k by computaton of the Slhouette coeffcent and preprocess the data set. Subsequently, outlers and mssng values are handled before nput to the K-Means method. The basc K-Means algorthm [7] can be descrbed as follows.. Select k ponts as ntal centrods. 2. Repeat 3. from k clusters by assgnng each pont to ts closest centrod. 4. Recompute the centrod of each cluster. 5. untl centrods do not change. Herarchcal clusterng technques are a second mportant type of clusterng method [7]. In the current study, Agglomeratve Herarchcal clusterng s appled to segment the busness tourst data set. Agglomeratve Herarchcal clusterng starts wth each pont as a cluster. The closest pars of clusters are merged untl only one cluster remans. The proxmty measurement between clusters s computed by dfferent methods. In our case, four most of the commonly used methods, namely the Sngle, Complete, Average and Ward methods, are used to measure the proxmty canddate. The basc Agglomeratve Herarchcal clusterng technque [7] can be descrbed as follows.. Compute the proxmty matrx, f necessary. 2. Merge the closest two clusters. 3. Update the proxmty matrx to reflect the proxmty between the new cluster and the orgnal clusters. 4. Repeat step 2 and 3 untl only one cluster remans. O. Kask et al. [6] suggested that an SOM could be used n dfferent felds ncludng market segmentaton. The SOM algorthm was proposed by Kohonen n 982 [8]. It uses a feed-forward neural network algorthm whch conssts of nput and output layers. The goal of SOM s to fnd a set of centrods and to assgn each pont n the data set to the centrod that provdes the best approxmaton of that pont [7]. The SOM procedure conssts of the followng stages.

3 336 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY 204. Randomly ntalze all weghts. 2. Select the nput vector. 3. Calculate the dstance between nput vectors and all weghts to fnd the closest output node. 4. Defne a neghborhood functon that allows the dentfcaton of the output node to be updated n the next step. 5. Update the wnner s weght. 6. Repeat steps 2 to 5 untl the algorthm converges. In ths study, we assessed the qualty of clusters by comparng ther Slhouette ndces [9]. The Slhouette wdth s a composte ndex reflectng the compactness and separaton of the clusters. The value of the Slhouette ndex les n the range of [-, ]. The best qualty of clusterng has a value near ; on the other hand, poorly clustered data have a value near -. The computaton of the Slhouette ndex s the average dssmlarty wthn ts own cluster (a ) compared to dssmlarty n the nearest neghborng cluster (b ) as follows. a = d p (a, x) b = C j r c j C j b a s = max(b a ) where d p s the dstance of vector to another vector n the same cluster. Further, x s an nterest vector. C j s j th cluster where j {,..., k} from the overall k cluster and C h s the h th cluster where h {,..., k} from the overall k cluster and h j. The best qualty of segmentaton and the best performance technque s ndcated by the maxmum value of the average Slhouette ndex calculated as follows. s = K K k = 2 n k n k = h j r c h s (, k ) d (a, x) B. Supervsed Learnng The market segmentaton results can be used for plannng market strateges. Ths study uses the segment label, whch each pont belong, as class labels n the supervsed learnng phase. The classfcaton technques used and tested n ths work were J48 Decson Tree, MLP, Naïve Bayes, Decson Table and OneR. J48 Decson Tree s wdely used n data classfcaton. It s modfed verson of C4.5 and mplemented n WEKA data mnng software [20]. We used the decson tree to predct the segments of busness toursts based on the nput varables. The decson tree s formed by splttng the source set nto subsets based on an attrbute value test and s completed when the subset at a node has all the same values of the target varable. The decson tree model conssts of one root, a number of branches, a number of nodes and a number of leaves. Each node nvolves one attrbute, each branch node represents a choce between a number of alternatves, and each leaf p () (2) node represents a class. Further computaton of ths method can be accessed n P.N. Tan et al. [7]. Multlayer Perceptron (MLP) s a modfcaton of the standard Perceptron. It s a popular form of the feedforward artfcal neural network model. In our case, t can be appled for data classfcaton by assgnng segment labels to represent each class. The MLP model conssts of three or more layers (an nput layer, one or more hdden layers, and an output layer). Each node n the MLP model s connected by weghts and output sgnals whch are a functon of the sum of the nputs to the node modfed by an actvaton functon. The MLP classfer s mplemented on WEKA wth tranng by the back-propagaton learnng algorthm whch s summarzed below [2].. Randomly ntalze all weghts. 2. Present the frst nput tranng vector to the network. 3. Propagate the nput vector through the network to obtan an output. 4. Calculate an error sgnal by comparng actual output to the target output. 5. Back-propagate the error sgnal through the network. 6. Adjust weghts to mnmze the overall error. 7. Repeat steps 2-7 wth the next nput vector untl the overall error s satsfactorly small. The Naïve Bayes algorthm s a classfcaton algorthm base on Bayes theorem. The Naïve Bayes Classfer gves hgh accuracy and speed when appled to the large data bases. It assumes the condtonal ndependence of attrbutes gven a class. The condtonal ndependence assumpton can be llustrated as follows. n, a2,..., an class) = P( a class) = P( a (3) P( a, a2,..., an class) = P( a class) P( a2 class)... P( an class) (4) Further calculatons of the Naïve Bayes algorthm are avalable n P.N. Tan et al.[7]. The decson table s one type of classfer that s mplemented on WEKA. It conssts of a set of features that s ncluded n the table, and class labels whch defned by the features. Creatng a decson table mght nvolve selectng some of the attrbutes. It searches the optmum feature subsets usng the best-frst search and uses cross-valdaton for evaluaton. Further detal on the decson table can be accessed n Ron Kohav s research [22]. OneR or the -Rule s a very smple classfcaton rule from a set of data. It generates a set of rules through all attrbute tests. Thus, each attrbute wll provde a dfferent set of rules, wth one rule for every value of the attrbute. The error rate of each attrbute rule s evaluated. OneR wll choose the attrbute that produces rules wth the least error rate. The algorthm of OneR [2] can be descrbed as follows.. Select an attrbute nput. 2. Buld a rule for each value of the attrbute by 2. Countng the frequent of each class appeared.

4 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY Fndng the most frequent class. 2.3 Makng the rule assgn that class to ths attrbute value. 3. Calculate the error rate of the rules. 4. Repeat steps -3 untl coverage of all attrbute-values. 5. Choose the rules wth the least error rate. To evaluate the performance of classfers, n our case, we dvde the busness tourst data set nto ten ndependent subsets (K=0) of sze n/0. Nne subsets were traned by the classfcaton technque and one subset was used for the test set. Tranng was repeated and tested ten tmes untl every subset was a test set. Accuracy of predcton was defned as the mean value over all the ten process tmes. The process s called K folds cross valdaton [7]. The performance of classfers s measured by the values of measurement as follows. Recall = (TP) / (TP+FN) (5) Precson = (TP) / (TP+FP) (6) FP Rate = (FP) / (FP+TN) (7) 2 (Precson Recall) F Measure= (Precson+ Recall) (8) where TP s the number of True Postve nstances, TN s the number of True Negatve nstances, FP s the number of False Postves nstances and FN s the number of False Negatve nstances. IV. EXPERIMENTAL DESIGN AND RESULTS A. Data Set The data set n ths paper conssts of 5,926 toursts who vsted Thaland for busness proposes and was selected from 72,43 tourst records for people who travelled to Thaland for many purposes durng the perod 2008 to 200. The overall tourst data set was obtaned from the Department of Toursm n the Mnstry of Toursm and Sports, Thaland [2]. The attrbutes of the data are the length of stay (days), age, annual ncomes (US Dollars), average expendture (Baht per day), type of accommodaton, occupatons, tourst orgn and place of resdence. The characterstcs of the busness toursts are presented n Appendx A. B. Study Framework The specfcaton of ths study requred the use of unsupervsed and supervsed learnng algorthms to partton the data nto segments and to predct the segments of unseen data, respectvely. Thus, the research desgn s dvded nto two phases namely an unsupervsed learnng phase and a supervsed learnng phase. The unsupervsed learnng phase nvolves parttonng by the best clusterng technque, whle the supervsed learnng phase nvolves usng segments to predct the cluster of new toursts by the best classfer. The study framework can be explaned as follows.. A data preprocessng step s crucal. In ths step, we removed the outlers, mssng values and unrelable data. Some attrbute values were transformed n order to qualfy for the requrements of the algorthms. Thus, the values of each element were normalzed n the range [0, ] and then de-normalzed after the processng was completed. 2. After the data had been preprocessed, the unsupervsed learnng phase began. We used SOM, K- Means and four of the most common herarchcal algorthms (Sngle, Complete, Average and Ward) to partton the busness tourst data set. The Slhouette ndex was calculated to assess all cluster parttons from 2 to 0. The best technque was selected. 3. After the unsupervsed learnng phase was fnshed, we analyzed the characterstcs of each cluster accordng to the results from the unsupervsed learnng phase, and predefned the cluster labels of toursts for the supervsed learnng phase. Busness Tourst Data Set (5,926 records) Data Preprocessng Busness Tourst Market Segmentaton HAC-Sngle HAC-Ward K-Means Results Analyss Assgn cluster labels to whch nstances belong Decson Tree Unsupervsed learnng phase HAC-Complete Supervsed learnng phase Busness Tourst Classfcaton Naïve Bayes Decson Table Concluson MLP HAC-Average SOM OneR Fg. Study framework 4. The toursts were assgned to segment labels accordng to the unsupervsed learnng results. The supervsed learnng phase was begun, usng the four canddates (J48 Decson Tree, MLP, Decson tree, One

5 338 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY 204 R and Naïve Bayes) to classfy the busness tourst data set. The performance of classfers was compared to fnd the best technque. The study framework s llustrated n Fg.. C. Unsupervsed Learnng Results In ths study, K-Means, a popular technque, was performed to partton the busness data compared wth the most commonly used herarchcal algorthms (Sngle, Complete, Average and Ward) and a SOM neural network. The optmum number of clusters was compared and s shown n Fg. 2 In general, the value of the Slhouette ndex, used to valdate the qualty of segmentaton, must be greater than 0.65 [23]. In ths study, the Slhouette ndex provded a lower value than ths threshold (0.65) because t was calculated on the normalzed value of the data. As we can see from Fg. 2, K-Means outperformed both SOM and the four most common methods of Herarchcal algorthm. Consequently, we chose the best partton obtaned from the K-Mean at fve clusters. The percentage of busness tourst per cluster s llustrated n Table. TABLE I PERCENTAGE OF BUSINESS TOURISTS PER CLUSTER Cluster Number of Percentage toursts per cluster contrbuton, , , Total 5, Table ndcates that segment 5 s relatvely domnant among the 5 clusters. Cluster 5 s the bggest cluster. It comprses over 35% (n=2,099) of overall toursts (N=5,926) and can be consdered as a homogeneous cluster. Cluster 4 s the smallest cluster and conssts of 7.9% (n=426) whch has a smlar profle of features. On the whole, busness toursts n all segments select hotels as ther accommodatons. Ther busness s manly n Bangkok. There was no sgnfcant dfference n the busness tourst age data. The characterstcs of the busness tourst market segment are summarzed as follows. Cluster conssts of 9.5% of overall busness toursts. Ths segment belonged to busness toursts who have an occupaton as clercal, sales or commercal. The busness toursts n ths cluster have the smallest mean age value. They manly come from Southeast Asa and East Asa wth 34.43% and 25.43%, respectvely. Moreover, they stayed n Thaland for a short perod (less than 0 days). The annual ncome of toursts n ths cluster s less than 40,000 US Dollars per year (26.2% less than 20,000 and 39.88% less than 40,000 US Dollars per year). Cluster 2 conssts of 6.5% of overall busness toursts. The toursts n ths cluster stay n Thaland for a short perod (less than 0 days). They have varety of occupatons and come from a varety of countres. The annual ncome of ths cluster s the least (42% less than 20,000 US Dollars per year) when comparng to the other segments. Fg. 2 Average Slhouette ndex provded by unsupervsed learnng partton from 2 to 0 clusters. Cluster 3 conssts of 2.73% of overall busness toursts. The toursts n ths cluster are admnstratve or manageral. They stay n Thaland for the shortest perod (less than 5 days) compared to other segments. They manly come from Southeast Asa and East Asa (32.30% and 30.43%, respectvely). The annual ncome of toursts n ths cluster s n 20,00 40,000 US Dollars per year (35.64%) and 40,00 60,000 US Dollars, respectvely. Cluster 4 s the smallest cluster. It conssts of only 7.9% of overall busness toursts. The toursts n ths cluster have the hghest annual ncome by mean (40,00-60,000 US Dollars) whch a varety of groups. They spend hghest daly expendture (4,000 6,000 Bath) by mean. All of the busness toursts n ths cluster are Europeans. Toursts n ths cluster are manly professonals (74.88%). Cluster 5 s the bggest cluster. It conssts of 35.42% of overall busness toursts. The toursts n ths cluster manly come from Southeast Asa (4.26%) and 99.95% of toursts n ths cluster are professonals. They stay n Thaland for less than 0 days. D. Supervsed Learnng Results In the supervsed learnng phase, the data classfcaton s controlled by a supervsed learnng technque. It s used to predct the segment of new busness toursts when the segments are produced. In the classfcaton phase, we used WEKA [20] verson 3.6 to learn from the tranng set. WEKA was employed on a Core 2 Duo processor wth 4 GB RAM. We compared the performance of the J48 Decson Tree, Decson Table, OneR, MLP and Naïve Bayes classfers. The class label of data was assgned accordng to whch cluster the tourst belonged as provded by the results of the clusterng phase. The correct classfcaton or msclassfcaton s determned by how well the tranng set can learn the pattern n the

6 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY data when compared to the test set whch comprses unseen data or new arrval tourst data. The performance of each classfer s suggested by the computaton of Precson, Recall, F-Measure and FP-Rate as shown n Table. 2. TABLE II COMPARING THE PERFORMANCES OF CLASSIFIERS Classfers Precson Recall F-Measure FP-Rate J Naïve Bayes OneR Decson Table MLP The expermental results ndcated that the Naïve Bayes classfer gves the hghest performance n terms of all four measurements. It gves slghtly better performance than the J48 decson tree and Decson Table, respectvely. It s clear that Naïve Bayes gves better results than MLP. Moreover, OneR produces a rule by choosng the occupaton of toursts but t provdes the least performance classfcaton when compared to the other four technques. Thus, Naïve Bayes can be used to predct the cluster of new busness toursts as part of the fve segments whch are provded by K-Means. The confuson matrx of the Naïve Bayes classfer, based on busness tourst varables, s presented n Table 3. TABLE III THE CONFUSION MATRIX OF NAÏVE BAYES CLASSIFIER Class Class2 Class3 Class4 Class5 Total Cluster, ,56 Cluster Cluster3 0 0, ,288 Cluster Cluster ,092 2,099 Total,47 962, ,00 5,926 Accuracy Wegh accuracy V. CONCLUSION Market segmentaton s an mportant tool, used for dvdng markets, whch are comprsed of ndvduals, nto smaller groups wth homogenous characterstcs wthn each segment, and heterogenety between segments, based on an dentfed set of attrbutes [3]. Ths study proposes a market segmentaton method for foregn toursts who come to Thaland for busness purposes. The method conssts of the evaluaton of unsupervsed learnng technques by computng the value of the average Slhouette ndex and comparng the performance of supervsed learnng technques. The unsupervsed learnng technques are compared usng K-Means, SOM neural network and Herarchcal (sngle, complete, average and ward method) clusterng. The expermental results ndcated that K-Means outperforms all of SOM and the four most common Herarchcal clusterng methods based on the busness tourst s varables. Each market segment has dfferent characterstcs. However, some attrbutes are not sgnfcant such as age of the toursts, whle some attrbutes nfluence each segment such as occupaton. Thus, the assocated organzatons can use the segmentaton method and the results to defne ther marketng plan or n other applcatons. Moreover, the supervsed technques can be used to predct a segment of new busness tourst arrvals. The classes of data are the segments of tourst that are provded by the unsupervsed learnng method. The supervsed learnng technques consst of J48Decson Tree, Decson Table, OneR, MLP and Naïve Bayes classfers. The expermental results ndcated that the Naïve Bayes performance better than the other technques. Although MLP, Decson Tree and Decson Table each gve a good classfcaton, they are not as good Naïve Bayes. Moreover, OneR gves the poorest performance of all the technques assessed. Thus, the assocated organzaton can use Naïve Bayes to predct the segments of new busness toursts as a part of the producton from clusterng technque. Future studes on the marketng segmentaton of toursm can focus on other nterestng markets and employ other related features such as cultural and socoeconomc varables. Some attrbutes can be summarzed to narrow wdth values whch occur and can add some related nformaton to the data. Moreover, rules mnng n the toursm data s challengng. ACKNOWLEDGMENT Ths work was supported by the Department of Computer Scence, Faculty of Scence, Kasetsart Unversty. REFERENCES [] W. Yotsawat and A. Srvhok, Data mnng of nternatonal tourst n Thaland by two step clusterng and classfcaton, Advanced Scence Letters, vol.20, pp , 204, do: 0.66/asl [2] Department of Toursm, Mnstry of Toursm and Sports, Thaland, [3] A. Kara and E. Kaynak, Markets of a sngle customer: explotng conceptual developments n market segmentaton, European Journal of Marketng, vol. 3, pp , 997, do: 0.08/ [4] D. Boone and M. Roehm, Retal segmentaton usng artfcal neural networks, Internatonal Journal of Research n Marketng, vol. 9, pp , September 2002, do: 0.06/S067-86(02) [5] S. Dolncar, Usng cluster analyss for market segmentaton typcal msconceptons, establshed methodologcal weaknesses and some recommendatons for mprovement, Australasan Journal of Market Research, vol., pp. 5-2, [6] M. Oja, S. Kask and T. Kohonen, Bblography of selforganzng map (SOM) papers: addendum, Neural Computng Surveys, vol. 3, pp. 56, 2003.

7 340 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY 204 [7] R.J. Kuo, L.M. Ho and C.M. Hu, Integraton of selforganzng feature map and K-means algorthm for market segmentaton, Computers and Operatons Research, vol. 29, pp , September 2002, do: 0.06/S (0) [8] J. Z. Bloom, Tourst market segmentaton wth lnear and non-lnear technques, Toursm Management, vol. 25, pp , December 2004, do: 0.06/j.tourman [9] M. Najm, A. Sharbatoghle and A. Jafareh, Toursm Market Segmentaton n Iran, Internatonal Journal of Toursm Research, vol. 2, pp , January 200, do: 0.002/jtr.768. [0] J. Chang, Segmentng toursts to aborgnal cultural festvals: An example n the Ruka trbal area, Tawan, Toursm Management, vol. 27, pp , December 2006, do: 0.06/j.tourman [] J. Km, S. We and H. Ruys, Segmentng the market of West Australan senor toursts usng an artfcal neural network, Toursm Management, vol. 24, pp , 2003, do: 0.06/S (02)00050-X. [2] E. M. Garca and M. R. Vela, Segmentaton of low-cost flghts users at secondary arports, Journal of Ar Transport Management, vol. 6, pp , July 200, do: 0.06/j.jartraman [3] M. Wedel and W.A. Kamakura, Market Segmentaton: Conceptual and Methodologcal Foundatons, 2 nd ed., Kluwer Academc Publshers, Norwell, [4] H. L. T. Trang and P. Kullada, Inbound toursm market segmentaton of the Andaman cluster, Thaland, M.S. thess, Prnce of Songkla Unv., Songkla, Thaland, [5] J. G. Brda, M. Dsegna and L. Ost, Segmentng vstors of cultural events by motvaton: A sequental non-lnear clusterng analyss of Italan Chrstmas market vstors, Expert Systems wth Applcatons, vol. 39, pp , October 202, do: 0.06/j.eswa [6] J. Vesanto, and E. Alhonem, Clusterng of the selforganzng map. IEEE Transactons on Neural Networks, vol., pp , 2000, do: 0.09/ [7] P.N. Tan, M. Stenbach and V. Kumar, Introducton to Data Mnng, Pearson Educaton, Inc., USA., [8] T. Kohonen, Self-Organzng Maps, Sprnger Inc., Berln, 995. [9] V. Lucas, J. G. B. C. Rcardo and R. H.Eduardo, On the Comparson of Relatve Clusterng Valdty Crtera, Statstcal Analyss and Data Mnng, vol. 3, pp , August 200. [20] WEKA: [2] Ian H. Wtten and Ebe Frank, Data mnng: practcal machne learnng tool and technques, 2 nd ed., Morgan Kaufmann Publshers, Calforna, USA, [22] R. Kohav, The power of decson tables, Lecture Notes n Computer Scence, vol. 92, pp , 995, do: 0.007/ _57. [23] W. Lu, X. D, G. Yang, H. Matsuzak, J. Huang, R. Me, et al., Algorthms for large-scale genotypng mcroarrays, Bonformatcs, vol. 9, pp , do: 0.093/bonformatcs/btg332. Anongnart Srvhok s an assocate professor at the Department of Computer Scence, Faculty of Scence, Kasetsart Unversty, Bangkok, Thaland. She has a doctorate degree n Informaton Systems from Central Queensland Unversty, Australa. Her research areas nclude data mnng, knowledge management, decson support systems and ntellectual captal. Wrot Yotsawat receved a B.S. n Computer Scence n 2007 from Walalak Unversty, Thaland. He s studyng at the Department of Computer Scence, Kasetsart Unversty, Thaland. Hs research nterests are data classfcaton, data clusterng and other related on data mnng aspects. APPENDIX A DESCRIPTIVE STATISTICS OF BUSINESS TOURIST ATTRIBUTES Varables Sample sze Percentage Varables Sample sze Percentage Length of stay (day) Orgn -5 3, Afrca , Amerca East Asa, Europe and above Mddle East Age (years) Oceana Less than South Asa South East Asa, , Annual ncome (US dollars) , Less than 20,000, , ,000-39,999, ,000-59,999, and above ,000-79, ,000 and above

8 JOURNAL OF SOFTWARE, VOL. 9, NO. 5, MAY APPENDIX A (CONT) DESCRIPTIVE STATISTICS OF BUSINESS TOURIST ATTRIBUTES Varables Sample sze Percentage Varables Sample sze Percentage Accommodaton Place of resdence Servce apartment Bangkok 4, Guest house Chonbur Hotel 5, Phuket Santarum/Nursng home Surat Than Resort ChangMa Average expendture (Baht per day) Krab 70.8 Less than 2, East (wthout Chonbur) ,000 4,000 2, Central (wthout Bangkok) ,000 6,000, West ,000 8, South (wthout Phuket, Krab and Surat Than) More than 8, North (wthout ChangMa) Occupaton Northeast 66. Professonal 2, Admnstratve and Manageral, Clercal, Salesmen and Commercal Person, Student and Chldren Housewfe, Retred and Unemployed 9.54 Laborer Other