• 응용통계연구
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• 제25권3호
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• 응용통계연구
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• 제20권1호
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• pp.167-181
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• 2007

유전자 발현 자료(gene expression data)는 전형적인 고차원 자료이며, 이를 분석하기 위한 여러 가지 군집 알고리즘(clustering algorithm)과 군집 결과들을 검증하는 군집타당성분석 기법(cluster validation technique)이 제안되고 있지만, 이들 군집 타당성을 분석하는 기법의 성능에 대한 비교, 평가는 매우 드물다. 본 논문에서는 저차원의 모의실험 자료와 실제 유전자 발현 자료에 대하여 군집 타당성분석 기법들의 성능을 비교하였으며, 그 결과 내적 측도에서는 Dunn 지수, Silhouette 지수 순으로 뛰어났고 외적 측도에서는 Jaccard 지수가 성능이 가장 우수한 것으로 평가되었다.

• 대한산업공학회지
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• 제29권2호
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• 디지털융복합연구
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• 제13권11호
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• pp.157-164
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• 2015

본 논문은 융복합을 위한 범주형 데이터의 부공간에 의한 군집화에 대해서 다룬다. 범주형 데이터는 수치형 데이터에만 국한되지 않기 때문에 기존의 범주형 데이터들의 평가척도들은 순서화(ordering)의 부재와 데이터의 고차원성과 희소성으로 인하여 한계를 가지기 마련이다. 따라서 각각의 군집에 존재하는 범주형 속성들의 상호 유사도을 보다 근접하게 측정할 수 있는 조건부 엔트로피 척도를 제안한다. 또한 군집의 최적화를 위하여 군집내의 발산을 최소화하고, 군집간의 독립성을 향상시킬 수 있는 새로운 목적함수를 제안한다. 제안된 알고리즘의 성능을 4개의 알고리즘과 비교검증하기 위하여 5가지의 데이터에 대하여 실험을 수행하였다. 비교검증을 위한 평가척도는 정확도, f-척도와 적응된 Rand 색인이다. 실험을 통하여 제안된 방법이 평가척도에 의한 결과에서 기존의 방법들보다 좋은 성능을 보였다.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.277-300
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• 2020

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover's distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1-score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.