• Journal of Microbiology and Biotechnology
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• 제16권5호
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• pp.748-756
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• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

• 경영과학
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• 제24권2호
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• pp.57-72
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• 2007

Many Applications of scientific research have coupled with functional data signal clustering techniques to discover novel characteristics that can be used for the diagnoses of several issues. In this article we present an interpretable multi-scale cluster hierarchy framework for clustering functional data using its multi-aspect frequency information. The suggested method focuses on how to effectively select transformed features/variables in unsupervised manner so that finally reduce the data dimension and achieve the multi-purposed clustering. Specially, we apply our suggested method to mutual fund returns and make superior-performing funds group based on different aspects such as global patterns, seasonal variations, levels of noise, and their combinations. To promise our method producing a quality cluster hierarchy, we give some empirical results under the simulation study and a set of real life data. This research will contribute to financial market analysis and flexibly fit to other research fields with clustering purposes.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1069-1077
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• 2014

클럽발은 발이 안쪽으로 굽어있는 상태로 태어나는 선천적인 발 기형의 일종이다. 본 연구에서는 한 쪽 클럽발 환자들의 수술 후 시간에 따른 양 쪽 발의 상대적인 차이 커브들을 군집분석 하려고 한다. 관측값들이 일정하지 않은 (irregular) 시점에서 희박하게 (sparsely) 관측되어서 일반적인 함수적 군집모형을 사용할 수 없어 James와 Sugar (2003) 가 제안한 희박한 자료의 함수적 군집 모형 (functional clustering model)을 이용하여 모수들을 추정하였다. 그리고 Sugar와 James (2003)의 왜곡함수 (distortion function)를 이용하여 군집의 수를 결정하여 군집분석하여 두 개의 군집을 발견하였다.

• 응용통계연구
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• 제32권1호
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• pp.149-160
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• 2019

본 연구는 2016년 7월부터 2017년 6월까지 인천 소재 A 대학교의 15분 단위의 일일 전기 사용량 시계열 데이터에 대해 functional data analysis 기법을 적용하여 군집화하고 각 군집의 특성을 파악하고 예측에 활용하고자 한다. 하루동안의 A 대학교의 전기 사용량은 패턴은 주중과 주말 에 큰 차이를 보이며 스플라인 기저함수로 FPCA 구한 후 이들에 대한 가우시안 분포의 혼합모형 기반 군집분석으로 3개의 군집화가 적절해 보인다. 각 군집에 대해 평균 함수, 확률밀도함수, 일들의 분포 등을 정리해 각 군집에 대한 정보와 특징을 보여준다.

• 제어로봇시스템학회논문지
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• 제12권10호
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• 한국통신학회논문지
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• 제35권7B호
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• pp.1050-1057
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• 2010

오늘날 네트워크에 대한 침해사고가 급증하고 있으며, 점차 증가하고 있는 인바운드 네트워크에 대한 공격도 함께 증가하고 있다. 이러한 공격에 대응하기 위해서 보안시스템의 개발이 지속적으로 이뤄지고 있지만, 인바운드 네트워크의 성능 감소의 문제가 발생하기 때문에, 성능 향상과 보안성 강화를 위한 모두를 고려한 보안시스템 개발이 시급한 실정이다[1]. 따라서 본 논문에서는 네트워크를 분할하여 보안등급에 따라 관리함으로써 성능을 향상 시키기 위한 보안클러스터링을 제안하고자 한다.

• Genomics & Informatics
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• 제5권3호
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• pp.124-128
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• 2007

Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.

• Molecules and Cells
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• 제25권2호
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• Genomics & Informatics
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• 제5권1호
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• pp.10-18
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• 2007

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using $Match^{TM}$ in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher’s exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.