• Culinary science and hospitality research
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• v.16 no.3
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• pp.94-113
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• 2010

Despite a number of previous studies about cultural tourism festivals, studies on food menus in the cultural tourism festival setting have often been neglected. Considering the importance of food menus, identifying major selection attributes that satisfy visitors in a festival setting is vital. Using conjoint analysis, this study demonstrated that price was the most influential selection attributes to attract visitors. The time required between ordering and receiving food was found to be the second important selection attribute, followed by menu and place. Cluster analysis identified two distinct segments that take different sets of elements into account when making their selection decision. Conjoint simulation estimated the most preferred foodservice form in cultural tourism festivals setting would have 21.18% potential market share. The implications gained from this study provided an important starting point for determining key selection attributes in establishing strategies to enhance visitors' level of satisfaction.

• Journal of the Korean Mathematical Society
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• v.36 no.4
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• pp.787-811
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• 1999

Let M be the maximal ideal space of the Banach algebra $H^{\infty}$ of bounded analytic functions on the open unit disc $\triangle$. For a positive singular measure ${\mu}\;on\;{\partial\triangle},\;let\;{L_{+}}^1(\mu)$ be the set of measures v with $0\;{\leq}\;{\nu}\;{\ll}\;{\mu}\;and\;{{\psi}_{\nu}}$ the associated singular inner functions. Let $R(\mu)\;and\;R_0(\mu)$ be the union sets of $\{$\mid$\psiv$\mid$\;<\;1\}\;and\;\{$\mid${\psi}_{\nu}$\mid$\;<\;0\}\;in\;M\;{\setminus}\;{\triangle},\;{\nu}\;\in\;{L_{+}}^1(\mu)$, respectively. It is proved that if $S(\mu)\;=\;{\partial\triangle}$, where $S(\mu)$ is the closed support set of $\mu$, then $R(\mu)\;=\;R0(\mu)\;=\;M{\setminus}({\triangle}\;{\cup}\;M(L^{\infty}(\partial\triangle)))$ is generated by $H^{\infty}\;and\;\overline{\psi_{\nu}},\;{\nu}\;{\in}\;{L_1}^{+}(\mu)$. It is proved that %d{\theta}(S(\mu))\;=\;0$ if and only if there exists as Blaschke product b with zeros $\{Zn\}_n$ such that $R(\mu)\;{\subset}\;{$\mid$b$\mid$\;<\;1}\;and\;S(\mu)$ coincides with the set of cluster points of $\{Zn\}_n$. While, we proved that $\mu$ is a sum of finitely many point measure such that $R(\mu)\;{\subset}\;\{$\mid${\psi}_{\lambda}$\mid$\;<\;1}\;and\;S(\lambda)\;=\;S(\mu)$. Also it is studied conditions on \mu for which $R(\mu)\;=\;R0(\mu)$.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.171-177
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• 2011

Accuracy and computing time are considerable issues in machine learning. In general, the computing time for data analysis is increased in proportion to the size of given data. So, we need a sampling approach to reduce the size of training data. But, the accuracy of constructed model is decreased by going down the data size simultaneously. To solve this problem, we propose a new statistical sampling method having similar performance to the total data. We suggest a rule to select optimal sampling techniques according to given data structure. This paper shows a sampling method for reducing computing time with keeping the most of accuracy using cluster sampling, stratified sampling, and systematic sampling. We verify improved performance of proposed method by accuracy and computing time between sample data and total data using objective machine learning data sets.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Journal of the Korean Society for information Management
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• v.29 no.1
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• pp.115-134
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• 2012

The author co-citation analysis is generally based on the frequency of the first author because most citation databases include only the first author in the bibliographic information. In this sense, the purpose of this study is to provide a better knowledge structure by utilizing the multiple authorship of author co-citation analysis. To achieve the purpose of this study, four different data sets are prepared: (1) counting the first author, (2) counting all the author without limiting the total frequency, (3) counting all the author with limiting the total frequency, and (4) counting adjusted frequencies based on the order of author subscription. The findings of this study show that there are clear differences between the knowledge structure counting all the author and the one counting only the first author. In addition, depending on the different methods, there are subtle changes of cluster members for authors.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.29-37
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• 2008

To transmit video bit stream over low bandwidth such as wireless channel, high compression algorithm like H.264 codec is exploited. In transmitting high compressed video bit-stream over low bandwidth, packet loss causes severe degradation in image quality. In this paper, a new algorithm for recovery of missing or erroneous motion vector is proposed. Considering that the missing or erroneous motion vectors in blocks are closely correlated with those of neighboring blocks. Motion vector of neighboring blocks are clustered according to average linkage algorithm clustering and a representative value for each cluster is determined to obtain the candidate motion vector sets. As a result, simulation results show that the proposed method dramatically improves processing time compared to existing H.264/AVC. Also the proposed method is similar to existing H.264/AVC in terms of visual quality.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.115-131
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• 2008

Small mobile devices have become commonplace in our everyday life, from cellular phones to PDAs. Discovering dense regions for the mobile devices is one of the problems of grate practical importance. It can be used in monitoring movement of vehicles, concentration of troops, etc. In this paper, we propose a novel algorithm on continuously clustering a large set of mobile objects. We assume that a mobile object reports its position only if it is too far away from the expected position and thus the location data received may be imprecise. To compute the location of each individual object could be costly especially when the number of objects is large. To reduce the complexity of the computation, we want to first cluster objects that are in proximity into a group and treat the members in a group indistinguishable. Each individual object will be examined only when the inaccuracy causes ambiguity in the final results. We conduct extensive experiments on various data sets and analyze the sensitivity and scalability of our algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.95-105
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• 2014

In this paper, we present a novel ant-based hierarchical clustering algorithm, where ants repeatedly hop from one node to another over a weighted directed graph of k-nearest neighborhood obtained from a given dataset. We introduce a notion of node pheromone, which is the summation of amount of pheromone on incoming arcs to a node. The node pheromone can be regarded as a relative density measure in a local region. After a finite number of ants' hopping, we remove nodes with a small amount of node pheromone from the directed graph, and obtain a group of strongly connected components as clusters. We iteratively do this removing process from a low value of threshold to a high value, yielding a hierarchy of clusters. We demonstrate the performance of the proposed algorithm with synthetic and real data sets, comparing with traditional clustering methods. Experimental results show the superiority of the proposed method to the traditional methods.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.11-18
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• 2010

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.