روش ترکیبی فضایی خوشه بندی داده ها برای انتخاب مکان : رویکرد داده محور از استخراج GIS

This article applies customer service to be the research background. Spatial data mining method is proposed to solve site selection of the service center. Firstly, a new data model for recording all the information of customer management is given, which transforms the traditional model-driven strategy to data-oriented method. Secondly, a hybrid spatial clustering method named OETTC–MEANS–CLASA algorithm is proposed. It has the advantages of applying k-means algorithm to reduce the result space and using simulated annealing method (CLASA) as result-searching strategy to find more qualified solutions. On the basis of GIS functions, we design deeper analytical function to take spatial obstacle factors, spatial environmental factors, spatial terrain factors, spatial traffic factors and cost factors into account. The result of the experiment declares that the algorithm does better at the both aspects of perform efficiency and result quality.

The site selection of the service center is the key factor that decides the efficiency of service response. The current research of location problem usually applies the linear programming to reach the optimization of time response and cost of the models (Aboolian, 2007, Berman and Krass, 2002, Jain and Vazirani, 2001 and Zhang, 2007), etc. But the above researches have two common shortages: (1) It should involve many spatial restriction factors, such as obstacle factors, environmental factors, traffic factors, terrain factors, etc. which require tremendous amount of variables and restrictions if it is solved by linear programming models. So it is quite difficult to build such a huge linear model for site selection. (2) The essence of the location problem is to find the global optimal k-centers or k distributions which is well known to be NP-hard ( Domı´nguez et al., 2008 and Owen and Daskin, 1998), etc. the linear model is not good enough to get the more qualitative results of k-centers/distributions. In this article, we choose customer management as the application field for illustration our method of the site selection of service centers, proposes a data-driven method instead of the model-oriented method, and explore a process for finding service sites of the enterprise from the huge amount of customer locations which are recorded as two-dimension points in GIS (geographical information system) (Koret & Koret, 1997). The huge amount of spatial points are regarded as service receivers which might be the current and potential customers of an enterprise. 1.1. The application of GIS in location problem Geographical information systems (GIS) have occupied the attention of many researches involving a number of academic fields including geography, civil engineering, computer science, land use planning, and environmental sciences (Domı´nguez et al., 2008). GIS can support a wide range of spatial queries that can be used to support location studies. Model application and model development are the major impact of GIS on the field of location science. These systems are designed to store, retrieve, manipulate, analyze, and map geographical data. GIS can serve as the source of input data for a location model. The applications of GIS in location problem only limit to the basic functions of visualization, querying, and preliminary analytical functions of overlapping, buffering, network analysis (Cheng et al., 2007, Koret and Koret, 1997 and Nikolakaki, 2004). Location–allocation model can also be integrated in GIS software to realize the resulting presentation in a real map (Cheng and Chang, 2001, Nathanail, 1998, Vlachopoulou et al., 2001 and Yeh and Chow, 1996). In all, the current researches focus on the basic functions of GIS, and the deeply analytical functions of spatial data have not been sufficiently developed. 1.2. The application of spatial clustering in location problem Clustering is the organization of a data set into homogenous and/or well separated groups with respect to a distance or, equivalently, a similarity measure (Tran et al., 2003). Spatial clustering, which groups data for finding all distribution patterns and interesting correlation among geographical data set, has numerous applications in pattern recognition (Ayala et al., 2006 and Domingo et al., 2002), spatial data analysis (Demir et al., 2007, Hu and Sung, 2006 and Lin, 2004), image processing (Chu, Roddick, & Pan, 2001), market research, etc. (Ester and Kriegel, 1998, Han et al., 2000 and Han and Kamber, 2000). Spatial data clustering is an important component of spatial data mining and further exploration of GIS functions (Ester & Kriegel, 1998). Spatial clustering method can be classified into four categories: partitioning method, hierarchical method, density-based method and grid-based method (Ester and Kriegel, 1998 and Zhang and Rushton, 2008). Since our research is to ensure the minimization of the overall travel distance of all the customers in the city. The clustering algorithms of hierarchical method, density-based method and grid-based method mainly focus on finding natural clusters which do not guarantee the minimization of distances to cluster centers. The partitioning algorithm is a good choice as a solution, the two typical types of partitioning algorithm are k-means and k-mediod ( Han et al., 2000 and Han and Kamber, 2000). The k-means algorithm uses the mean value of objects in a cluster as the cluster center. The objective criterion used in algorithm is squared-error function defined asIn the above formula, x is the point in space representing the given object, and mi is the mean value of cluster Ci. The method can be described as: Step 1: k-means algorithm arbitrarily choose k-centers/distributions as initial solutions. Step 2: k-means algorithm assigns each object to its nearest center forming a new set of cluster. Step 3: all the centers of those new clusters are then computed by taking the mean of all the objects in each cluster. The steps 2–3 are repeated until the criterion of function E does not change after an iteration. The k-means algorithm is relatively scalable and efficient in processing large data set because the computational complexity of the algorithm is O(n ∗ k ∗ t), where n is the total number of objects, k is the number of clusters, and t is the number of iterations. Normally, k ≪ n, t ≪ n. The method often terminates at local optimum ( Han et al., 2000 and Han and Kamber, 2000). Except for that, k-means algorithm is also very sensitive to noise and outlier data points since a small of such data can substantially influence the mean value. k-Mediod method uses the most centrally located object(mediod) in a cluster to be the cluster center instead of taking the mean value of the objects in a cluster ( Tung, Hou, & Han, 2001). Because of this, k-mediod is less sensitive to noise and outlier data, but it results in a higher running time. The typical k-mediod algorithm is called CLARANS (clustering large application based on randomized search) ( Han et al., 2000, Han and Kamber, 2000, Ng and Han, 2002 and Chu et al., 2002). When searching for a better center in step 3 of k-means algorithm, CLARANS tries to find a better solution by randomly picking one of the k-centers and replacing it with anther randomly chosen object from other (n − k) objects, and if no better solution is found after a certain number of attempts, the local optimum is assumed to be reached. Though CLARANS is more effective than other k-mediod algorithm, its computational cost is as high as O(n ∗ n), where n is the number of objects. 1.3. Section arrangement On the basis of GIS function and the research of spatial data clustering method, we intent to improve the current method for better solving the problem of site selection. In Section 2, a data model will be proposed to comprehensively organize the information of the management process of customer service which is the key aspect to realize our idea of data-driven method. The section includes the modeling method and operating method of the spatial data cube for introducing the spatial analytical function to the solution of site selection. In Section 3, the computation method of spatial distance between service center and service receiver is proposed, it takes the obstacle factor and the location weight into account. In Section 4, a hybrid spatial clustering method named OETTC–MEANS–CLASA algorithm is designed to realize the mining oriented method for location selection of customer service center. Obstacle factor, environmental factor, traffic factor, terrain factor and cost factor are taken into consideration in the process of the algorithm. Finally a series of experiments will be carried out to test the efficiency and the quality of our algorithm.

The article proposes a hybrid spatial clustering method for the selection of customer service location. We transform the traditional model-oriented method to data-driven method, establish a data model named spatial data cube for customer management, and explore a process of site selection from the huge amount of objects which are recorded as two-dimension points in GIS. OETTC–MEANS–CLASA is proposed in consideration of spatial obstacle factors, spatial environmental factors, spatial traffic factors, spatial terrain factors and cost factors. It provides a new decision making process for the problem of site selection. Our experiments have proved that OETTC–MEANS–CLASA is more efficient and can achieve more qualified results than other spatial clustering methods.