• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.143-145
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• 2005

In this paper, an empirical study result on pattern estimation method is devoted to reveal underlying data patterns with a relatively reduced computational cost. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). Conventional SAHN based clustering requires large computation time in the initial step of algorithm. To deal with this concern, we modified overall process with a partial approach. In the beginning of this method, we divide given data set to several sub groups with uniform sampling and then each divided sub data group is applied to SAHN based method. The advantage of this method reduces computation time of original process and gives similar results. Proposed is applied to several test data set and simulation result with conceptual analysis is presented.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.437-438
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• 2018

문서 군집화 성능을 높이기 위한 한 방법으로 차원 축소를 적용한 문서 벡터로 군집화를 실시하는 방법이 있다. 본 발표에서는 특이값 분해(SVD), 커널 주성분 분석(Kernel PCA), Doc2Vec 등의 차원 축소 기법을, K-평균 군집화(K-means clustering), 계층적 병합 군집화(hierarchical agglomerative clustering), 스펙트럼 군집화(spectral clustering)에 적용하고, 그 성능을 비교해 본다.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1507-1510
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• 2003

We propose UPGMA(Unweighted Pair Group Method using Average distance) as hierarchical clustering to detect abrupt shot changes using multiple features such as pixel-by-pixel difference, global and local histogram difference. Conventional $\kappa$-means algorithm which is a method of the partitional clustering, has to select an efficient initial cluster center adaptively UPGMA that we propose, does not need initial cluster center because of agglomerative algorithm that it starts from each sample for clusters. And UPGMA results in stable performance. Experiment results show that the proposed algorithm works not only well but also stably.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.1-7
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• 2020

Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.369-380
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• 2004

Clustering algorithms with different similarity measures are commonly used to find an optimal clustering or close to original clustering. The recovery level of using Euclidean distance and distances transformed from correlation coefficients is evaluated and compared using Rand's (1971) C statistic. The C values present how the resultant clustering is close to the original clustering. In simulation study, the recovery level is improved by applying the correlation coefficients between objects. Using the data set from Spellman et al. (1998), the recovery levels with different similarity measures are also presented. In general, the recovery level of true clusters was increased by using the correlation coefficients.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.683-696
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• 2005

The results of using principal coordinates prior to cluster analysis are investigated on the samples from multiple binary outcomes. The retrieval ability of the known clustering algorithm is significantly improved by using principal coordinates instead of using the distance directly transformed from four association coefficients for multiple binary variables.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.697-699
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• 2002

본 논문에서는 복합명사에 대한 색인 방법을 다각적으로 적용하여 계층적 결함 문서 클러스터링 시스템의 결과를 분석하고자 한다. 우선 한글 색인 엔진과 HAC(Hierarchical Agglumerative Clustering) 엔진에 대해서 설명하고 한글 색인엔진에서 제공되는 세가지 복합명사 분석 모드에 대해서 설명한다. 또한 구현된 클러스터링 엔진의 특징과 속도 향상을 위한 기법 등을 설명한다. 실험에서는 다양한 요소를 가지고 클러스터링된 문서 집합에 대한 분석 결과를 보인다. 실험 결과에 대한 분석에서 복합명사에 대한 색인 방법이 문서 클러스터링의 결과에 직접적인 영향을 준다는 것을 보여준다.