• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.269-277
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• 2000

We propose two new types multi-stage blind clustering equalizers for QAM demoulation, which are called a complex classification algorithm(CCA) and a radial basis function algorithm(RBFA). The CCA uses a clustering technique based on the joint gaussian probability function and computes separately the real part and imaginary part for simple implementation as well as less computation. In order to improve the performance of CCA, the Dual-Mode CCA(DMCCA) incorporates the CCA tap-updating mode with the decision-directed(DD) mode. The RBFA reduces the number of cluster centers through three steps using the classification technique of RBF and then updates the equalizer taps for QAM demodulation. Test results on 16-QAM confirm that the proposed algorithms perform better the conventional multi-state equalizers in the senses of SER and MSE under multi-path fading channel.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.428-431
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• 2004

A multi-stage hierarchical clustering technique, which is an unsupervised technique, has been proposed in this paper for classifying the hyperspectral data .. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using a context-free similarity measure. This study applied the multistage hierarchical clustering method to the data generated by band reduction, band selection and data compression. The classification results were compared with them using full bands.

• Proceedings of the KSRS Conference
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• v.1
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• pp.188-190
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• 2006

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The local segmentor of the first stage performs regiongrowing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. This stage uses a sliding window strategy with boundary blocking to alleviate a computational problem in computer memory for an enormous data. The global segmentor of the second stage has not spatial constraints for merging to classify the segments resulting from the previous stage. The experimental results show that the new approach proposed in this study efficiently performs the segmentation for the images of very large size and an extensive number of bands

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.357-365
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• 2005

The typical patterns of surface $O_3$ pollution and their dependence on meteorology were studied in the Greater Seoul Area (GSA) during warm season (April-September) from 1998 to 2002. In order to classify the $O_3$ pollution patterns, two-stage (average linkage then k-means) clustering technique was employed based on daily maximum $O_3$ concentrations obtained from 53 monitoring sites during high $O_3$ events (118 days). The clustering technique identified four statistically distinct $O_3$ pollution patterns representing the different horizontal distributions and levels of $O_3$ in GSA. The prevailed pattern (93 days, $49.5\%$) distinctly showed the gradient of $49.5\%$ concentrations going from west to east in GSA. Very high $49.5\%$ concentrations throughout GSA (24 days, $12.8\%$) were also found as a significant pattern of severe $O_3$ pollution. In order to understand the characteristics of $O_3$ pollution patterns, the relationship between $O_3$ pollution patterns and meteorological conditions were analyzed using both synoptic charts and surface/upper air data. Each pattern was closely associated with surface wind interacted with synoptic background flow allowing to transport and accumulate $O_3$ and its precursor. In particular, the timing and inland penetration of sea-breeze were apparently found to play very important role in determining $O_3$ distributions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.572-579
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• 2003

With the expansion of e-commerce, E-marketplace has become one of the most discussed topics in recent years. Limited theoretical works, however, have been done to optimize the practical use of e-marketplace systems. Other potential issues aside, this research has focused on this problem: 'the participants waste too much time, effort and cost to find out their best partner in B2B marketplace.' To solve this problem, this paper proposes a system which provides the user-company with the automated and customized brokering service. The system proposed in this paper assesses the weight on the priorities of a user-company, runs the two-stage clustering algorithm with self-organizing map and K-means clustering technique. Subsequently, the system shows the clustering result and user guide-line. This system enables B2B marketplace to have more efficiency on transaction with smaller pool of partners to be searched.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• The Journal of Society for e-Business Studies
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• v.10 no.2
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.76-83
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• 2009

This paper presents a compression method for animated meshes or mesh sequences which have a shared connectivity and geometry streams. Our approach is based on static semi-regular mesh compression algorithm introduced by Khodakovky et al. Our encoding algorithm consists of two stages. First, the proposed technique creates a semi-regular mesh sequence from an input irregular mesh sequence. For semi-regular remeshing of irregular mesh sequences, this paper adapts the MAPS algorithm. However, MAPS cannot directly be performed to the input irregular mesh sequence. Thus, the proposed remesh algorithm revises the MAPS remesher using the clustering information, which classify coherent parts during the animation. The second stage uses wavelet transformation and clustering information to compress geometries of mesh sequences efficiently. The proposed compression algorithm predicts the vertex trajectories using the clustering information and the cluster transformation during the animation and compress the difference other frames from the reference frame in order to reduce the range of 3D position values.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.529-542
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• 2000

Recent years, the use of multimedia information is rapidly increasing, and the video media is the most rising one than any others, and this field Integrates all the media into a single data stream. Though the availability of digital video is raised largely, it is very difficult for users to make the effective video access, due to its length and unstructured video format. Thus, the minimal interaction of users and the explicit definition of video structure is a key requirement in the lately developing image and video management systems. This paper defines the terms and hierarchical video structure, and presents the system, which construct the clustering-based video hierarchy, which facilitate users by browsing the summary and do a random access to the video content. Instead of using a single feature and domain-specific thresholds, we use multiple features that have complementary relationship for each other and clustering-based methods that use normalization so as to interact with users minimally. The stage of shot boundary detection extracts multiple features, performs the adaptive filtering process for each features to enhance the performance by eliminating the false factors, and does k-means clustering with two classes. The shot list of a result after the proposed procedure is represented as the video hierarchy by the intelligent unsupervised clustering technique. We experimented the static and the dynamic movie videos that represent characteristics of various video types. In the result of shot boundary detection, we had almost more than 95% good performance, and had also rood result in the video hierarchy.