• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of the Korean Geographical Society
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• v.38
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• pp.15-36
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• 1988

In this paper, I consider the development of methods in contemporary human geography in terms of a dialectical relation of action and structure, and try to draw a new horizon of method toward which geographical research and spatial theory would develop. The positivist geography which was dominent during 1960s has been faced both with serious internal reflections and strong external criticisms in the 1970s. The internal reflections that pointed out its ignorance of spatial behavior of decision-makers and its simplication of complex spatial relations have developed behavioural geography and systems-theoretical approach. Yet this kinds of alternatives have still standed on the positivist, geography, even though they have seemed to be more real and complicate than the previous one, The external criticisms that have argued against the positivist method as phenomenalism and instrumentalism suggest some alternatives: humanistic geography which emphasizes intention and action of human subject and meaning-understanding, and structuralist geography which stresses on social structure as a totality which would produce spatial phenomena, and a theoretical formulation. Human geography today can be characterized by a strain and conflict between these methods, and hence rezuires a synthetic integration between them. Philosophy and social theory in general are in the same in which theories of action and structural analysis have been complementary or conflict with each other. Human geography has fallen into a further problematic with the introduction of a method based on so-called political ecnomy. This method has been suggested not merely as analternative to the positivist geography, but also as a theoretical foundation for critical analysis of space. The political economy of space with has analyzed the capitalist space and tried to theorize its transformation may be seen either as following humanistic(or Hegelian) Marxism, such as represented in Lefebvre's work, or as following structuralist Marxism, such as developed in Castelles's or Harvey's work. The spatial theory following humanistic Marxism has argued for a dialectic relation between 'the spatial' and 'the social', and given more attention to practicing human agents than to explaining social structures. on the contray, that based on structuralist Marxism has argued for social structures producing spatial phenomena, and focused on theorising the totality of structures, Even though these two perspectives tend more recently to be convergent in a way that structuralist-Marxist. geographers relate the domain of economic and political structures with that of action in their studies of urban culture and experience under capitalism, the political ecnomy of space needs an integrated method with which one can overcome difficulties of orthhodox Marxism. Some novel works in philosophy and social theory have been developed since the end of 1970s which have oriented towards an integrated method relating a series of concepts of action and structure, and reconstructing historical materialism. They include Giddens's theory of structuration, foucault's geneological analysis of power-knowledge, and Habermas's theory of communicative action. Ther are, of course, some fundamental differences between these works. Giddens develops a theory which relates explicitly the domain of action and that of structure in terms of what he calls the 'duality of structure', and wants to bring time-space relations into the core of social theory. Foucault writes a history in which strategically intentional but nonsubjective power relations have emerged and operated by virtue of multiple forms of constrainst wihthin specific spaces, while refusing to elaborate any theory which would underlie a political rationalization. Habermas analyzes how the Western rationalization of ecnomic and political systems has colonized the lifeworld in which we communicate each other, and wants to formulate a new normative foundation for critical theory of society which highlights communicatie reason (without any consideration of spatial concepts). On the basis of the above consideration, this paper draws a new norizon of method in human geography and spatial theory, some essential ideas of which can be summarized as follows: (1) the concept of space especially in terms of its relation to sociery. Space is not an ontological entity whch is independent of society and has its own laws of constitution and transformation, but it can be produced and reproduced only by virtue of its relation to society. Yet space is not merlely a material product of society, but also a place and medium in and through which socety can be maintained or transformed.(2) the constitution of space in terms of the relation between action and structure. Spatial actors who are always knowledgeable under conditions of socio-spatial structure produce and reproduce their context of action, that is, structure; and spatial structures as results of human action enable as well as constrain it. Spatial actions can be distinguished between instrumental-strategicaction oriented to success and communicative action oriented to understanding, which (re)produce respectively two different spheres of spatial structure in different ways: the material structure of economic and political systems-space in an unknowledged and unitended way, and the symbolic structure of social and cultural life-space in an acknowledged and intended way. (3) the capitalist space in terms of its rationalization. The ideal development of space would balance the rationalizations of system space and life-space in a way that system space providers material conditions for the maintainance of the life-space, and the life-space for its further development. But the development of capitalist space in reality is paradoxical and hence crisis-ridden. The economic and poltical system-space, propelled with the steering media like money, and power, has outstriped the significance of communicative action, and colonized the life-space. That is, we no longer live in a space mediated communicative action, but one created for and by money and power. But no matter how seriously our everyday life-space has been monetalrized and bureaucratised, here lies nevertheless the practical potential which would rehabilitate the meaning of space, the meaning of our life on the Earth.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Korean Association of Arts Management
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• no.58
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• pp.5-47
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• 2021

This paper is a story about five middle-aged men in their 50s who suddenly began their photographic activities as they reached middle age. In the perspective of Borudieu's cultural capital theory, this study observes five men in their 50s by implementing in-depth interviews about the motivation behind taking photographs, the experience of photography activities, and the rewards of these activities. The theory has undergone a theoretical revision with the criticism that factors other than the class can be influential. Based on these ideas, I have proceeded my study by preferentially grasping the notion of the 'field' in accordance with the specific history of Korean society. Therefore, this study sought to more specifically understand the various photographic activities of middle-class men in their 50s by referring Coskuner-Balli and Thompson's argument(2013), which revised 2018's cultural captial theory and proposed the concept of 'subordinate cultural capital' and 'leisure capital' who proposed by Backlund, E. A. & Kuentzel, W. F.(2013). As a middle-class men in their 50s, research participants have grown up and worked in a social atmosphere where economic capital is recognized as an individual's ability. However, they are faced with the value that the knowledge and taste towards culture and arts is one's identity. In addition to the subjective deprivation that arises from this situation, the lifespan characteristic of their age that it is on the brink of the old age appeared to have influenced them to put their psychological motivation immediately into practice. Economic capital was the main conversion terms to move form interest to practice, which includes 'time' as a resource as well as money. With the cultural practices being expanded since their creation of photographs, the reason that these expansions can be maintained more actively lies in their identity as 'cultural artist' that is consolidated in new relationships in the sharing of photographic activities. In this way, photographic activities grant a symbolic status of 'a middle-aged man who actively builds and expresses his identity' through the conversion of accumulating cultural capital and the conversion into social capital. Furthermore, the recognized scope of the symbolic capital acquired by the research participants is in the domain of the private life that is family and acquaintance. Especially, they were gaining a great psychological reward from their children's recognition that they are not just a 'breadwinner' but 'dad who cultivates himself with a culture and arts'. Accordingly, by considering that 'generation' other than class can be a meaningful discussion point when understanding Korea society from the perspective of cultural theory, this study is meaningful that a more flexible understanding of cultural theory can give a glimpse into the possibility of a more specific and diverse approach that will arise in the discussion of culture and arts education.

• (The)Study of the Eastern Classic
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• no.34
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• pp.63-88
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• 2009

This paper was a quest of pattern of holding "Dae Hak - the Great Learning" during Joseon Period having investigated the characteristics of the Essentials of the Studies of the Sages(聖學輯要, Seong Hak Jibyo) that was compiled by Lee I was a reinterpretation of the Great Learning, and also academic enrichment. During the period of Joseon Dynasty, the Great Learning had held the most important position as core scripture in the intellectual society that pursued Seong Hak(聖學, sage learning). Throughout the Joseon Period, the Great Learning was the essential text for the Emperorship Learning(帝王學, Jewang Hak) as well as Seong Hak, and it can also be said that Seong Hak Jibyo compiled by Yulgok - the courtesy name of Lee I, was the comprehensive collections thereof. While compiling Seong Hak Jibyo, Yulgok presented a model of Seong Hak of Joseon, which was based on "the Great Learning". Yul Gok organized the system of "Seong Hak Jibyo" largely in five parts, and properly arranged the Three Cardinal Principles(三綱領, samgangryeong) and Eight Articles or Steps(八條目, paljomok) therein. Particularly, in the Chapter Two, "Cultivating Oneself(修己, sugi)", Yulgok deal with 'being able to manifest one's bright virtue'(明明德, myeong myeong deok) among the Three Cardinal Principles as the core curriculum, meanwhile, Yulgok also covered "Investigation of things, gyeongmul(格物)," "Extension of knowledge, chiji(致知)," "Sincerity of the will, Seongui(誠意)," "Rectification of the mind, Jeongshim(正心)," "Cultivation of the personal life, susin(修身)," among Paljomok(eight steps) as the ultimate purpose of 'Stopping in perfect goodness'(止於至善, jieojiseon) These well preserve the principal system of Confucianism where "Cultivating oneself and regulating others (修己治人, sugichiin)" are core value, and his instructions as such also back up academic validity logically by presenting specific guidelines for practice according to each domain. Reinterpretation of "The Great Learning" by Yulgok in Seong Hak Jibyo is an arena to investigate the characteristics of Confucianism in Joseon Period, which was different from that of China, furthermore, such guidelines might take a role as criteria to understand the characteristics of humans and learning possessed by Korean people.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.