Clustering the Clinical Course of Chronic Urticaria Using a Longitudinal Database: Effects on Urticaria Remission

예영민,윤지원,Woo Seong-Dae,Jang Jae-Hyuk,Lee Youngsoo,이현영,신유섭,남동호,박해심 대한천식알레르기학회 2021 Allergy, Asthma & Immunology Research Vol.13 No.3

Purpose Little is known about the clinical course of chronic urticaria (CU) and predictors of its prognosis. We evaluated CU patient clusters based on medication scores during the initial 3 months of treatment in an attempt to investigate time to remission and relapse rates for CU and to identify predictors for CU remission. Methods In total, 4,552 patients (57.9% female; mean age of 38.6 years) with CU were included in this retrospective cohort study. The K-medoids algorithm was used for clustering CU patients. Kaplan-Meier survival analysis with Cox regression was applied to identify predictors of CU remission. Results Four distinct clusters were identified: patients with consistently low disease activity (cluster 1, n = 1,786), with medium-to-low disease activity (cluster 2, n = 1,031), with consistently medium disease activity (cluster 3, n = 1,332), or with consistently high disease activity (cluster 4, n = 403). Mean age, treatment duration, peripheral neutrophil counts, total immunoglobulin E, and complements levels were significantly higher for cluster 4 than the other 3 clusters. Median times to remission were also different among the 4 clusters (2.1 vs. 3.3 vs. 6.4 vs. 9.4 years, respectively, P < 0.001). Sensitization to house dust mites (HDMs; at least class 3) and female sex were identified as significant predictors of CU remission. Around 20% of patients who achieved CU remission experienced relapse. Conclusions In this study, we identified 4 CU patient clusters by analyzing medication scores during the first 3 months of treatment and found that sensitization to HDMs and female sex can affect CU prognosis. The use of immunomodulators was implicated in the risk for CU relapse. Purpose Little is known about the clinical course of chronic urticaria (CU) and predictors of its prognosis. We evaluated CU patient clusters based on medication scores during the initial 3 months of treatment in an attempt to investigate time to remission and relapse rates for CU and to identify predictors for CU remission. Methods In total, 4,552 patients (57.9% female; mean age of 38.6 years) with CU were included in this retrospective cohort study. The K-medoids algorithm was used for clustering CU patients. Kaplan-Meier survival analysis with Cox regression was applied to identify predictors of CU remission. Results Four distinct clusters were identified: patients with consistently low disease activity (cluster 1, n = 1,786), with medium-to-low disease activity (cluster 2, n = 1,031), with consistently medium disease activity (cluster 3, n = 1,332), or with consistently high disease activity (cluster 4, n = 403). Mean age, treatment duration, peripheral neutrophil counts, total immunoglobulin E, and complements levels were significantly higher for cluster 4 than the other 3 clusters. Median times to remission were also different among the 4 clusters (2.1 vs. 3.3 vs. 6.4 vs. 9.4 years, respectively, P < 0.001). Sensitization to house dust mites (HDMs; at least class 3) and female sex were identified as significant predictors of CU remission. Around 20% of patients who achieved CU remission experienced relapse. Conclusions In this study, we identified 4 CU patient clusters by analyzing medication scores during the first 3 months of treatment and found that sensitization to HDMs and female sex can affect CU prognosis. The use of immunomodulators was implicated in the risk for CU relapse.

Nearest neighbor and validity-based clustering

Seo H. Son,Suk T. Seo,Soon H. Kwon 한국지능시스템학회 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.3

The clustering problem can be formulated as the problem to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. The author proposes a nearest neighbor and validity-based clustering algorithm where each data point in the data set is linked with the nearest neighbor data point to form initial clusters and then a cluster in the initial clusters is linked with the nearest neighbor cluster to form a new cluster. The linking between clusters is continued until no more linking is possible. An optimal set of clusters is identified by using the conventional cluster validity index. Experimental results on well-known data sets are provided to show the effectiveness of the proposed clustering algorithm.

2022 FIBA 남자농구 아시아컵 경기기록을 통한 선수들의 군집분석

예원진,이성노,유덕수 한국체육과학회 2023 한국체육과학회지 Vol.32 No.5

The purpose of this study is to determine whether there is a difference in performance level among participating players and the participation of star players in the 30th Men's Basketball Asia Cup in 2022, based on box score data provided on the official Asian Cup website. Clustering was performed using k-means cluster analysis, one of the machine learning techniques. The subject of this study was the game records of 189 players out of 16 teams participating in the tournament obtained through the official records of the 2022 Men's Basketball Asian Cup tournament, and differences in the players' performance characteristics were compared through a total of 21 variables. This study used the statistical program Python version 3.10.1 along with the library to perform cluster analysis. All significance levels for statistical analysis were set to .05, and the results obtained were as follows. First, as a result of cluster analysis using official records provided by the 2022 Men's Basketball Asian Cup competition, players from each country at the Basketball Asian Cup could be classified into three clusters. Second, there was a statistically significant difference in performance by cluster between cluster 1, cluster 2, and cluster 3, and the post-hoc test results showed that the players in cluster 2 had the best performance. Next, it was confirmed in the order of cluster 3 > cluster 1. Third, the abnormality detection results showed that there were 9 abnormal values among the cluster analysis results. Looking at this, ‘Q. Zhou’, ‘H. EHaddadi’, ‘A. Al Dwairi’, ‘G. ‘RA’, ‘D. Chism’, ‘A. Alderazi’, ‘W. Artino’, ‘M. Bolden’, ‘W. Arakji’ 9 were found to be basketball players. Fourth, the outlier players and all remaining players are divided into points, shots made, shot attempts, 2-point shots made, 2-point shots attempted, free throws made, free throw attempts, offensive rebounds, defensive rebounds, turnovers, There was a significant difference in block and efficiency. Summarizing the results of this study, from the perspective of a leader, strategies and tactics for the next men's basketball Asia Cup tournament can be designed and implemented based on the characteristics of the players in each group. On the player side, based on the performance characteristics of players in each group, it is possible to determine the individual's performance level and the gap in performance with other excellent players in this competition.

Emergence of East Asian TFT-LCD Clusters: A Comparative Analysis of the Samsung Cluster in South Korea and the Chimei Cluster in Taiwan

윤진효,박상문,임동욱,함성득 기술경영경제학회 2010 ASIAN JOURNAL OF TECHNOLOGY INNOVATION Vol.18 No.1

This paper investigates cluster formation and the development processes of new thin file transistor liquid crystal display (TFT-LCD) clusters in East Asia. Despite the pivotal role of clusters in regional development and national competitiveness, there are only a few studies on how new East Asian high-tech clusters have emerged and evolved and how these clusters are similar to and different from other clusters. Based on a comparative analysis of new TFT-LCD clusters between Samsung in Asan-Tangjung, South Korea, and Chimei in Tainan, Taiwan, we examine dynamic development processes and investigate how these rural areas have changed into high-tech clusters in only a decade’s time. Specifically, this paper explores the preconditions and initiation characteristics of TFT-LCD clusters. It also compares some similarities and differences between two East Asian TFT-LCD clusters and investigates the uniqueness of other global clusters. Therefore, this paper enhances our understanding of the dynamics of industrial clusters, adds a comparative perspective on cluster analysis, and suggests policy implications from the case study of cluster formation in South Korea and Taiwan.

K-means based Clustering Method with a Fixed Number of Cluster Members

Yi, Faliu,Moon, Inkyu Korea Multimedia Society 2014 멀티미디어학회논문지 Vol.17 No.10

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

Nearest neighbor and validity-based clustering

Son, Seo H.,Seo, Suk T.,Kwon, Soon H. Korean Institute of Intelligent Systems 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.3

The clustering problem can be formulated as the problem to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. The author proposes a nearest neighbor and validity-based clustering algorithm where each data point in the data set is linked with the nearest neighbor data point to form initial clusters and then a cluster in the initial clusters is linked with the nearest neighbor cluster to form a new cluster. The linking between clusters is continued until no more linking is possible. An optimal set of clusters is identified by using the conventional cluster validity index. Experimental results on well-known data sets are provided to show the effectiveness of the proposed clustering algorithm.

k-중앙개체 군집방법을 이용한 한국 프로농구선수의 군집화

한수철,전수영,진서훈 한국자료분석학회 2008 Journal of the Korean Data Analysis Society Vol.10 No.6

주어진 데이터의 개체들을 비슷한 특징을 가지는 소그룹으로 나누어 그 그룹들의 특징이나 대표성을 찾는 분석 과정을 군집분석이라고 한다. 군집분석은 크게 분리 군집방법과 계층적 군집방법으로 구분할 수 있다. 본 연구에서는 계층적 군집방법을 이용하여 군집의 수를 정하고, 분리 군집방법 중 하나인 k-중앙개체 군집방법을 적용하여 한국 프로농구선수들의 군집화를 시도해 보았다. 프로농구선수들의 데이터는 몇몇 변수들에 있어 특이치가 존재하기 쉽다. 따라서 이런 경우에는 특이치에 영향을 크게 받는 k-평균 군집방법을 적용하는 것보다는 특이치에 덜 민감한 k-중앙개체 군집방법의 활용이 좋은 결과를 줄 수 있다. k-중앙개체 군집방법의 구현을 위해 PAM(partitioning around medoids) 알고리즘을 이용하였다. 군집분석결과 3개의 군집으로 선수들을 군집화하였고, 각 군집의 특징을 파악하였다. Cluster analysis is one of statistical methods for finding groups so that objects in the same group are similar each other and objects in the different group are dissimilar. There are two distinctive techniques in cluster analysis. One is hierarchical method the other is partitioning method. In this study, we built the clusters from the data of korean professional basketball players. The hierarchical method was used for finding the proper number of clusters and k-medoids clustering which is one of partitioning method was used for building clusters. The professional basketball players data generally has outliers in several variables. Therefore, instead of applying k-means clustering for this kind of data, k-medoids clustering which is not affected a lot by outliers can give a better result than that of k-means clustering. In order to implement k-medoids clustering PAM(partitioning around medoids) algorithm was used. The resulting clusters are obtained as three distinguished clusters and the characteristics of each cluster are summarized.

유아 동기 유형 평가도구 적용 군집화 및 사례분석

유구종 ( Yoo Ku Jong ),최승연 ( Choi Seung Yeon ),조희정 ( Cho Hee Jung ),성은영 ( Sung Eun Young ) 한국어린이문학교육학회 2017 어린이문학교육연구 Vol.18 No.1

본 연구는 유아 동기 유형 평가도구에 의해 평가가 실시되었을 때 유아의 동기 유형이 어떠한 하위 군집의 유형으로 분류 되는지 살펴보고 구체적인 사례를 통해 하위군집의 특성을 살펴봄으로써 현장 적용성을 비롯한 생태학적 타당도를 제고하는데 그 목적이 있다. 이를 위해 본 연구에서는 614명의 유아를 대상으로 유아 동기 유형 평가도구를 적용하여 군집분석을 실시하여 군집화 된 군집유형을 명명하고, 성별과 연령에 따라 군집 유형 차이를 검증하였다. 또한 만 5세 유아 20명으로 구성된 1개 학급을 대상으로 군집화를 통해 도출한 군집 유형으로 연구대상을 유형별로 나누어 군집유형에 따른 행동 특성을 파악하고자 사례분석을 실시하였다. 이를 위해 계층적 군집분석, K-means 군집분석의 총 2회에 걸친 군집분석을 통해 3개의 군집으로 유아의 동기 유형을 군집화 하였다. 군집 1은 동기 유형에 있어 내적 동기가 월등히 높고 외적 동기와 무동기는 매우 낮은 것으로 나타나 고(高)내적 동기형으로 명명되었고, 군집 2는 내적 동기가 군집 1과 군집 3에 비하여 절반수준에 머물렀고, 외적 동기와 무동기의 경우에도 군집 3에 비해 저조한 수준으로 나타나 저(低)동기형으로 명명하였다. 군집 3의 경우는 내적 동기는 군집 1과 비슷한 수준으로 나타났으나 무동기 유형이 가장 높게 나타나 복합동기형으로 명명하였다. 성별과 연령에 따른 차이분석은 모두 차이가 없는 것으로 나타나 동기는 연령과 성별에 관계없는 개인의 일반적인 특성인 것으로 나타났다. 사례분석에서는 군집분석을 통해 군집화 된 군집별 특징이 사례에 부합되어 발현되는 것으로 나타났다. For clustering of infant motivation types, hierarchical cluster analysis and K-means cluster analysis were conducted. As a result, infant motivation types were classified into 3 clusters. As for the motivation types of cluster 1, internal motivation was significantly outstanding while external motivation and internal motivation were quite insignificant. Internal motivation of cluster 2 was as low as a half of those of cluster 1 and cluster 3. External motivation and no-motivation as well were lower than those of cluster 3. Internal motivation of cluster 3 was similar to that of cluster 1, but the level of no-motivation was the highest. Accordingly, in this study, cluster 1 is called the high internal motivation type, cluster 2 the low motivation type, and cluster 3 the complex motivation type, respectively, to represent the attributes of the clusters. As a result of analyzing the differences among the cluster types depending on the sex and age, it was determined that there was no significant difference depending on the sex and age of the infant. As a case study on the clusters of motivation types, anecdote recordings were conducted for 12 weeks and analyzed systematically. As a result, it was determined that young children in the high internal motivation type cluster were seeking self-satisfaction and satisfying their internal curiosity while young children in the low motivation type were following the instruction of others and expected extrinsic rewards. Young children in the complex motivation type cluster were sensitive to external stimuli and would act at a moderate level.

주파수 및 시간 특성을 활용한 머신러닝 기반 공동주택 주거소음의 군집화 및 분류

김정훈,이송미,김수홍,송은성,류종관 한국음향학회 2023 韓國音響學會誌 Vol.42 No.6

본 연구는 주파수 및 시간 특성을 활용하여 머신러닝 기반 공동주택 주거소음의 군집화 및 분류를 진행하였다. 먼저, 공동주택 주거소음의 군집화 및 분류를 진행하기 위하여 주거소음원 데이터셋을 구축하였다. 주거소음원 데이터셋은 바닥충격음, 공기전달음, 급배수 및 설비소음, 환경소음, 공사장 소음으로 구성되었다. 각 음원의 주파수 특성은 1/1과 1/3 옥타브 밴드별 Leq와 Lmax값을 도출하였으며, 시간적 특성은 5 s 동안의 6 ms 간격의 음압레벨 분석을 통해Leq값을 도출하였다. 공동주택 주거소음원의 군집화는 K-Means clustering을 통해 진행하였다. K-Means의 k의 개수는 실루엣 계수와 엘보우 방법을 통해 결정하였다. 주파수 특성을 통한 주거소음원 군집화는 모든 평가지수에서 3개로군집되었다. 주파수 특성 기준으로 분류된 각 군집별 시간적 특성을 통한 주거소음원 군집화는 Leq평가지수의 경우 9 개, Lmax 경우는 11개로 군집되었다. 주파수 특성을 통해 군집된 각 군집은 타 주파수 대역 대비 저주파 대역의 음에너지의 비율 또한 조사되었다. 이후, 군집화 결과를 활용하기 위한 방안으로 세 종류의 머신러닝 방법을 이용해 주거소음을 분류하였다. 주거소음 분류 결과, 1/3 옥타브 밴드의 Leq값으로 라벨링된 데이터에서 가장 높은 정확도와 f1-score 가 나타났다. 또한, 주파수 및 시간적 특성을 모두 사용하여 인공신경망(Artificial Neural Network, ANN) 모델로 주거소음원을 분류했을 때 93 %의 정확도와 92 %의 f1-score로 가장 높게 나타났다. In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, Leq and Lmax values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both Leq and Lmax analysis. Temporal characteristic clustered residential noise source into 9 clusters for Leq and 11 clusters for Lmax. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with Leq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

Cluster or Diversify? A Dilemma for Sustainable Local Techno-Economic Development

Fred Phillips,Deog-Seong Oh,Eung-Hyun Lee 세계과학도시연합 2016 World Technopolis Review Vol.5 No.2

By highlighting the efficiencies gained from regional specialization, the cluster concept has distracted economic development officials from their traditional role of diversifying regional and local economies. Clustering was a viable strategy for much of the 18 years following its original appearance in the literature. Now, two events cast doubt on the continued viability of cluster-based specialization. First, the digital convergence has blurred the boundaries that once separated one industry from another. An industry cluster strategy becomes difficult when the industry cannot be defined. Second, many cluster initiatives fail. Combining literature search with the system-theoretic notions of efficiency and redundancy, we find many factors moderate cluster success. This implies regions facing uncertain success in their cluster-building efforts should thoroughly understand their unique circumstances and build upon them. Regions with successful clusters are advised to aim for multiple related clusters or superclusters.