• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.127-134
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• 2010

In continuous vocabulary recognition system by probability distribution of clustering method has used model parameters of an advance estimate to generated each contexts for phoneme data surely needed but it has it's bad points of gaussian model the accuracy unsecure of composed model for phoneme data. To improve suggested probability distribution mixed gaussian model to optimized that phoneme data search supported configuration thread system. This paper of configuration thread system has used extension facet classification user phoneme configuration thread information offered gaussian model the accuracy secure. System performance as a result of represent vocabulary dependence recognition rate of 98.31%, vocabulary independence recognition rate of 97.63%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2603-2608
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• 2009

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the clustering method based NMF(non-negative matrix factorization) and refinement of documents in cluster by using coherence of cluster. The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.11-18
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• 2008

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user's requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user's requirement for document classification and the document clusters by means of machine. The Proposed method can improve the quality of document clustering because the re-weighted terms. the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• ETRI Journal
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• v.34 no.6
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• pp.922-931
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• 2012

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy-efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near-optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near-optimal energy-efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy-efficient routing technique produces a longer network lifetime and achieves better energy efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.41-44
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• 2006

The purpose of state tying is to reduce the number of models and to use relatively reliable output probability distributions. There are two approaches: one is top down clustering and the other is bottom up clustering. For seen data, the performance of bottom up approach is better than that of top down approach. In this paper, we propose a new clustering technique that can enhance the undertrained triphone clustering performance. The basic idea is to tie unreliable triphones before clustering. An unreliable triphone is the one that appears in the training data too infrequently to train the model accurately. We propose to use monophone distance to preprocess these unreliable triphones. It has been shown in a pilot experiment that the proposed method reduces the error rate significantly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.10-14
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• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.