• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.215-218
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• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.

• Journal of KIISE:Software and Applications
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• v.26 no.9
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• pp.1114-1124
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• 1999

클러스터링은 동일한 클러스터에 속하는 데이타들 간에는 유사도가 크도록 하고 다른 클러스터에 속하는 데이타들 간에는 유사도가 작도록 주어진 데이타를 몇 개의 클러스터로 묶는 것이다. 어떤 대상을 기술하는 데이타는 수치 속성뿐만 아니라 정성적인 비수치 속성을 갖게 되고, 이들 속성값은 관측 오류, 불확실성, 주관적인 판정 등으로 인해서 정확한 값으로 주어지지 않고 애매한 값으로 주어지는 경우가 많다. 본 논문에서는 애매한 값을 퍼지값으로 표현하는 수치 속성과 비수치 속성을 포함한 데이타에 대한 비유사도 척도를 제안하고, 이 척도를 이용하여 퍼지값을 포함한 데이타에 대하여 퍼지 클러스터링하는 방법을 소개한 다음, 이를 이용한 실험 결과를 보인다. Abstract The objective of clustering is to group a set of data into some number of clusters in a way to minimize the similarity between data belonging to different clusters and to maximize the similarity between data belonging to the same cluster. Many data for real world objects consist of numeric attributes and non-numeric attributes whose values are fuzzily described due to observation error, uncertainty, subjective judgement, and so on. This paper proposes a dissimilarity measure applicable to such data and then introduces a fuzzy clustering method for such data using the proposed dissimilarity measure. It also presents some experiment results to show the applicability of the proposed clustering method and dissimilarity measure.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.473-483
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• 1999

We introduce a way to construct a supercomputing capable system with some networked PCs, running the Linux operating system and computing power comparable with expensive commercial workstations, and with the Parallel Virtual Machine (PVM) software which enables one to control the total CPUs and memories of the networked PCs. By benchmarking the system using a PVM parallel program, we find that the system's parallel efficiency is close to 90 %.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.625-630
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• 2014

The amount of information is increasing rapidly with the development of the internet and the computer. Since these enormous information is managed by the document forms, it is necessary to search and process them efficiently. The document clustering technique which clusters the related documents through the similarity between the documents help to classify, search, and process the large amount of documents automatically. This paper proposes a method to find the initial seed points through principal component analysis when the documents represented by vectors in the feature vector space are clustered by K-means algorithm in order to increase clustering performance. The experiment shows that our method has a better performance than the traditional K-means algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.569-572
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• 2018

본 연구는 영화 포스터를 대상으로 OpenCV를 활용하여 k-means clustering 기반의 색감을 분석하는 기법을 제안한다. 또한 이를 활용하여 영화 포스터 간의 유사도를 구하고 특정 영화와 대표색을 가지는 영화를 추천하는 시스템을 제안한다. 이를 위해 본 연구에서 다음과 같은 가정을 기반으로 한다. 첫 번째, 포스터는 해당 영화를 가장 잘 나타내는 이미지로, 포스터의 색감은 영화의 전반적인 분위기를 가진다. 두 번째, 영화 사이에 유사한 색감을 가진다면, 해당 영화들은 유사한 분위기를 가진다. 본 연구에서는 2단계로 나누어 연구를 진행한다. 우선 k-means clustering 기법을 통하여 데이터를 전처리 하여 영화별 대표색을 선정한다. 이 때, 선정된 대표색을 이용하여 각 영화간 색감 유사도를 분석한 결과를 통해, 같은 장르의 영화도는 유사도가 높음을 확인할 수 있었다. 다음으로 앞의 색감 유사도 분석을 통하여 특정 영화와 높은 유사도를 가지는 영화를 추천한다. 본 연구에서 추천된 영화는 기존의 영화 선택 기준에 비하여 사용자 본인의 취향을 반영한다. 본 연구 내용이 영화를 추천하는 과정에서 반영된다면 추천 시스템의 정확도와 사용자 만족도 향상에 기여할 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.9
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• pp.4025-4029
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• 2015

Background: Gastrointestinal tract cancers are among the most common cancers in Iran and comprise approximately 38% of all the reported cases of cancer. This study aimed to describe the epidemiology and to investigate spatial clustering of common cancers of the gastrointestinal tract across the counties of Iran using full Bayesian smoothing and Moran I Index statistics. Materials and Methods: The data of the national registry cancer were used in this study. Besides, indirect standardized rates were calculated for 371 counties of Iranand smoothed using Winbug 1.4 software with a full Bayesian method. Global Moran I and local Moran I were also used to investigate clustering. Results: According to the results, 75,644 new cases of cancer were nationally registered in Iran among which 18,019 cases (23.8%) were esophagus, gastric, colorectal, and liver cancers. The results of Global Moran's I test were 0.60 (P=0.001), 0.47 (P=0.001), 0.29 (P=0.001), and 0.40 (P=0.001) for esophagus, gastric, colorectal, and liver cancers, respectively. This shows clustering of the four studied cancers in Iran at the national level. Conclusions: High level clustering of the cases was seen in northern, northwestern, western, and northeastern areas for esophagus, gastric, and colorectal cancers. Considering liver cancer, high clustering was observed in some counties in central, northeastern, and southern areas.

• KSBB Journal
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• v.20 no.2 s.91
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• pp.71-75
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• 2005

Alignment of 2D-gel images of biological samples can visualize the difference of expression profiles and also inform us candidates of protein spots to be further analyzed. However, comparison of two proteome images between the case and control does not always successfully identify differentially expressed proteins because of sample-to-sample variation, poor reproducibility of 2D-gel electrophoresis and inconsistent electrophoresis conditions. Multiple alignment of 2D-gel image must be preceded before visualizing the difference of expression profiles or clustering proteome images. Thus, a software for the alignment of multiple 2D-Gel images and their clustering was developed by applying various algorithms and statistical methods. Microsoft Visual C++ was used to implement the algorithms in this work. Multiresoultion-multilevel algorithm was found out to be suitable for fast alignment and for largely distorted images. Clustering of 10 different proteome images of Fetal Alcohol Syndrome, was carried out by implementing a k-means algorithm and it gave a phylogenetic tree of proteomic distance map of the samples. However, the phylogenetic tree does not discriminate the case and control. The whole image clustering shows that the proteomic distance is more dependent to age and sex.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.681-691
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• 2006

Clustering is one of widely used methods for grouping thousands of genes by their similarities of expression levels, so that it helps to analyze gene expression profiles. This method has been used for identifying the functions of genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles because single gene might involve multiple genetic functions. Clustering methods, however, have the problems that they are sensitive to initialization and can be trapped into local optima. To solve these problems, this paper proposes an evolutionary fuzzy clustering method, where adaptive a-cut based evaluation is used for the fitness evaluation to apply different criteria considering the characteristics of datasets to overcome the limitation of Bayesian validation method that applies the same criterion to all datasets. We have conducted experiments with SRBCT and yeast cell-cycle datasets and analyzed the results to confirm the usefulness of the proposed method.