• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1571-1580
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• 1999

When a hierarchical structure is derived from data set for data mining and machine learning, using a conceptual clustering algorithm, one of the unsupervised learning paradigms, it is not unusual to have a different set of outcomes with respect to the order of processing data objects. To overcome this problem, the first classification process is proceeded to construct an initial partition. The partition is expected to imply the possible range in the number of final classes. We apply center sorting to the data objects in the classes of the partition for new data ordering and build a new partition using ITERATE clustering procedure. We developed an algorithm, REIT that leads to the final partition with stable and best partition score. A number of experiments were performed to show the minimization of order bias effects using the algorithm.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.726-734
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• 2004

This paper presents a facial animation method that enables the user to select a sequence of facial frames from the facial expression space, whose level of details the user can select hierarchically Our system creates the facial expression space from about 2400 captured facial frames. To represent the state of each expression, we use the distance matrix that represents the distance between pairs of feature points on the face. The shortest trajectories are found by dynamic programming. The space of facial expressions is multidimensional. To navigate this space, we visualize the space of expressions in 2D space by using the multidimensional scaling(MDS). But because there are too many facial expressions to select from, the user faces difficulty in navigating the space. So, we visualize the space hierarchically. To partition the space into a hierarchy of subspaces, we use fuzzy clustering. In the beginning, the system creates about 10 clusters from the space of 2400 facial expressions. Every tine the level increases, the system doubles the number of clusters. The cluster centers are displayed on 2D screen and are used as candidate key frames for key frame animation. The user selects new key frames along the navigation path of the previous level. At the maximum level, the user completes key frame specification. We let animators use the system to create example animations, and evaluate the system based on the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2550-2556
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• 2012

We proposed the energy-efficient routing algorithm ALPS (Ad hoc network Localized Positioning System) algorithm that is range-free based on the distance information. The routing coordinate method of ALPS algorithm consists of hierarchical cluster routing that provides immediately relative coordinate location using RSSI(Received Signal Strength Indication) information. Existing conventional DV-hop algorithm also to manage based on normalized the range free method, the proposed hierarchical cluster routing algorithm simulation results show more optimized energy consumption sustainable path routing technique to improve the network management.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.4
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• pp.40-48
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• 2007

MANET constructs a network that is free and independent between sensor nodes without infrastructure. Also, there are a lot of difficulties to manage data process, control etc.. back efficiently from change of topology by transfer of sensor node that compose network. Especially, because each sensor node must consider mobility certainly, problem about energy use happens. To solve these problem, mechanisms that compose cluster of cluster header and hierarchic structure between member were suggested. However, accompanies inefficient energy consumption because sensing power level of sensor node is fixed and brings energy imbalance of sensor network and shortening of survival time. In this paper, I suggested intelligent clustering mechanism for efficient energy management to solve these problem of existent Clustering mechanism. Proposed mechanism corresponds fast in network topology change by transfer of sensor node, and compares in existent mechanism in circumstance that require serial sensing and brings elevation survival time of sensor node.Please put the abstract of paper here.

• Convergence Security Journal
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• v.15 no.3_1
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• pp.75-82
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• 2015

Recently, lot of researches on multi-level protocol have been done to balance the sensor node energy consumption of WSN and to improve the node efficiency to extend the life of the entire network. Especially in multi-hop protocol, a variety of models have been studied to improve energy efficiency and apply it in real system. In multi-hop protocol, we assume that energy consumption can be adjusted based on the distance between the sensor nodes. However, according to the physical property of the actual WSN, it's hard to establish this. In this paper, we propose low-power sub-cluster protocol to improve the energy efficiency based on the spread of distance. Compared with the previous protocols, the proposed protocol is energy efficient and can be effectively used in the wireless sensing network.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.529-531
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• 2005

이 논문에서는 용어 클러스터링의 성능에 직접적인 영향을 주는 자질 확장에 따른 시스템의 성능 변화를 보았다. 객관적인 성능 비교를 위하여 용어 클러스터링 결과와 한국어 의미 계층망에서 추출한 클러스터를 비교하였다. 실험 결과, 용어의 뜻 풀이말을 자질로 사용한 경우보다 자질을 확장한 방법(Bigram, Case)이 성능이 좋게 나왔으며, 자질확장 시에 사용되는 말뭉치의 추출방법에 따라 다른 성능을 보였는데, 단순히 Bigram 정보를 사용하여 확장한 것 보다는 동사의 격 관계(Case)정보를 이용한 것이 성능이 좋게 나왔다.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.60-70
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• 2018

A software defined storage (SDS) is being deployed in cloud environment to allow multiple users to virtualize physical servers, but a solution for optimizing space efficiency with limited physical resources is needed. In the conventional data deduplication system, it is difficult to deduplicate redundant data uploaded to distributed storages. In this paper, we propose a distributed deduplication method using similarity-based clustering and multi-layer bloom filter. Rabin hash is applied to determine the degree of similarity between virtual machine servers and cluster similar virtual machines. Therefore, it improves the performance compared to deduplication efficiency for individual storage nodes. In addition, a multi-layer bloom filter incorporated into the deduplication process to shorten processing time by reducing the number of the false positives. Experimental results show that the proposed method improves the deduplication ratio by 9% compared to deduplication method using IP address based clusters without any difference in processing time.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.911-921
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• 2004

Accurate minutiae extraction from input fingerprint images is one of the critical modules in robust automatic fingerprint identification system. However, the performance of a minutiae extraction is heavily dependent on the quality of the input fingerprint images. If the preprocessing is performed according to the fingerprint image characteristics in the image enhancement step, the system performance will be more robust. In this paper, we propose a knowledge-based preprocessing method, which extracts S features (the mean and variance of gray values, block directional difference, orientation change level, and ridge-valley thickness ratio) from the fingerprint images and analyzes image quality with Ward's clustering algorithm, and enhances the images with respect to oily/neutral/dry characteristics. Experimental results using NIST DB 4 and Inha University DB show that clustering algorithm distinguishes the image Quality characteristics well. In addition, the performance of the proposed method is assessed using quality index and block directional difference. The results indicate that the proposed method improves both the quality index and block directional difference.

• The Journal of Bigdata
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• v.6 no.2
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• pp.29-38
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• 2021

Deviation of route in aviation safety management is a dangerous factor that can lead to serious accidents. In this study, the anomaly score is calculated by classifying the tracks through clustering and calculating the distance from the cluster center. The study was conducted by extracting tracks within 100 km of the airport from the ADS-B track data received for one year. The wake was vectorized using linear interpolation. Latitude, longitude, and altitude 3D coordinates were used. Through PCA, the dimension was reduced to an axis representing more than 90% of the overall data distribution, and k-means clustering, hierarchical clustering, and PAM techniques were applied. The number of clusters was selected using the silhouette measure, and an abnormality score was calculated by calculating the distance from the cluster center. In this study, we compare the number of clusters for each cluster technique, and evaluate the clustering result through the silhouette measure.