• Communications for Statistical Applications and Methods
• /
• v.29 no.1
• /
• pp.103-125
• /
• 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.

• Korean Journal of Adult Nursing
• /
• v.21 no.6
• /
• pp.705-717
• /
• 2009

Purpose: The purpose of this study was to identify symptom clusters in patients with breast cancer and to investigate the associations among them with functional status and quality of life (QOL). Methods: A convenient sample of 303 patients was recruited from an oncology-specialized hospital. Results: Two distinct clusters were identified: A gastrointestinal- fatigue cluster and a pain cluster. Each cluster significantly influenced functional status and QOL. Based on these two clusters, we identified subgroups of symptom clusters using K-means cluster analysis. Three relatively distinct patient subgroups were identified in each cluster: mild, moderate, and severe group. Disease-related factors (i.e., stage, metastasis, type of surgery, current chemotherapy, and anti-hormone therapy) were associated with these subgroups of symptom clusters. There were significant differences in functional status and QOL among the three subgroups. The subgroup of patients who reported high levels of symptom clusters reported poorer functional status and QOL. Conclusion: Clinicians can anticipate that breast cancer patients with advanced stage, metastasis, and who receive mastectomy, and chemotherapy will have more intense gastrointestinal-fatigue or pain symptoms. In order to enhance functional status and QOL for patients with breast cancer, collective management for symptoms in a cluster may be beneficial.

• Korean Management Science Review
• /
• v.24 no.2
• /
• pp.57-72
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.149-160
• /
• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.41 no.5
• /
• pp.371-380
• /
• 2008

This study elucidated the floral composition of marine algae and community structure at Hallyeo-haesang National Park, on the southern coast of Korea. In all, 89 species, comprising 10 green, 30 brown and 49 red algae, were identified. The dominant species in terms of importance value were Ulva pertusa, Colpomenia sinuosa, Undaria pinnatifida, Padina arborescens, Hizikia fusiformis, Sargassum sagamianum and Amphiroa dilatata. The vertical distribution of marine vegetation was characterized by Enteromorpha linza - Ulva pertusa - Gelidium divaricatum in the upper intertidal zone, Hizikia fusiformis - Sargassum thunbergii in the middle intertidal zone, and Amphiroa spp. - Hildenbrandtia rubra - Corallina pilulifera in the lower intertidal zone. Functional form group analysis showed that coarsely branched forms comprised 50.3% of the algal community, whereas thick leathery forms, sheet forms and filamentous forms comprised 11.9-13.1%. R/P, C/P and (R+C)/P values were 1.91, 0.74 and 2.64, respectively. A cluster analysis of species occurrence suggested that the number of marine algal species differed greatly among the sampling sites.

• Genomics & Informatics
• /
• v.5 no.1
• /
• pp.10-18
• /
• 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.

• Fashion & Textile Research Journal
• /
• v.18 no.5
• /
• pp.606-616
• /
• 2016

We intend an empirical assessment of examining the differences in the appearance management behaviors and demographic variables among groups classified by the clothing consumption values. The questionnaires are administered to 493 female and male adults above 20 years old in Seoul, Gyeonggi-do, Daegu and Kyungpook regions. For analysis of data from 478 respondents, descriptive statistics, cluster analysis, Cronbach's ${\alpha}$, ANOVA, Duncan test and ${\chi}^2$ test were applied. We show the following results. First, Factor analyses were employed for the clothing consumption values and appearance management behaviors. Six factors were for clothing consumption values: Individuality, appearance attractive, social, functional, conditional and fashion clothing consumption value. Four factors were for appearance management behaviors: weight training, skin care, hair care, make-up and clothing selection. According to clothing consumption values, four groups were classified: the passive, functional, social, and active group. We did cluster analysis to the appearance management behaviors of weight training, skin care, hair care, make-up and clothing selection. Second, the social and active groups were more interested in individuality, appearance attractive, social, functional, conditional and fashion clothing value. And they were also more involved in appearance management behaviors. Third, among the demographic variables, the single and female in 20s and 30s with higher level of education belonged to the active group. In this contribution, we find significant differences in the appearance management behavior and demographic variables classified by the clothing consumption values.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.30 no.6
• /
• pp.128-140
• /
• 2003

The purpose of this research was to present characteristics for the classification of biotopes and classification method of biotopes as basic data for ecological landscape planning in Metropolitan Daegu. The results of this study were as follows. 1) The study identified fifteen characteristics for classification of biotopes. Ecological landscape characteristics were divided into structural and functional factors. There are six structural factors such an inclination, and nine functional factors such as temperature. 2) The study area was separated into sixty eight biotope types. For example, an industrial district was divided into two biotope types: a biotope type of an industrial district with abundant green space, and a biotope type of an industrial district with scarce green space. 3) In the result of cluster analysis using the average linkage method between groups, biotope groups were divided into fifteen clusters and biotope groups were divided into seven clusters. Each cluster was named according to the features of a descriptive statistics analysis. For example, cluster 8 was identified as a biotope type with an impermeable pavement rate of more than 90 percent and an afforestation rate under 10 percent. 4) Fifteen biotope groups were converted to land use patterns for remote application and utilization of urban biotope in city planning. Biotope groups of a building area beyond an intermediate floor with an afforestation rate under 20-30 percent was converted to a land use pattern such as a tall apartment complex or commercial district. When examining the characteristics that were established in this research, there was a limit to achieve the objective of grade-classification because of a lack of related basic data. The research of landscape ecological characteristics for the classification of biotopes could not be completed due to a lack of time and resources, thus the study of ecological landscape characteristics will be accomplished over time.

• Proceedings of the Korean Society of Sericultural Science Conference
• /
• 2003.10a
• /
• pp.24-27
• /
• 2003

The ribosomal RNA gene (rDNA) cluster was located in the nucleolus organizer and was genetically determined as one locus. We speculated by using sequence differences in the functional rDNA unit that the segregation time between Chinese and Japanese types of B. mandarina is about three million years ago. The differences of the amount of inserted non-LTR retrotransposons, R1Bm and R2Bm, in rDHA cluster were used for the identification of B.mori strains. (omitted)

• Journal of Korean Society of Rural Planning
• /
• v.21 no.1
• /
• pp.129-141
• /
• 2015

This study classifies the types of spatio-functional differentiation in Korean island areas and analyses typical characters and suggests the development directions by each type. Eup/Myeon-level island areas are classified as six types by the factor analysis and the cluster analysis. First type is the traditional rural center. This type puts emphasis on maintaining phase as the central space and has to maximize development potential of the whole of settlement zone. Second type is the specialized region in manufacturing industry and the qualitative mutual growth of regional industries is able to be suggested. Third type is the specialized region in the neighborhood service provision. This type needs to devise the plan for utilizing potential customers actively and developing into the region specialized in tourism industry. Fourth type is the specialized region in tourism-support service functions. This type has to promote differentiated policies for maintaining amenity infra or value of countryside capital and preservation and utilization of resources by regional features. Fifth type is the fishing industry-dominated region. This type has to promote sustainable fishery development through the policy reflecting regional features and condition. Finally, sixth type is the sluggish region dominated with the traditional agriculture and fishery. This type is needed to aim at developing into the new food production base having the advantage of clean environment by strengthening support in specialized agro-fishery products. The existing researches on spatio-functional differentiation were mostly discussed with respect to land development, but this study highlights the difference in deal with the island areas distinguished from the condition of industry.