• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.661-671
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• 2005

The aim of this study is to propose a clustering method, called tree-structured clustering, by recursively partitioning continuous multivariate dat a based on overall $R^2$ criterion with a practical node-splitting decision rule. The clustering method produces easily interpretable clustering rules of tree types with the variable selection function. In numerical examples (Fisher's iris data and a Telecom case), we note several differences between tree-structured clustering and K-means clustering.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.503-512
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• 2012

Detecting outliers in a linear regression model eventually fails when similar observations are classified differently in a sequential process. In such circumstances, identifying clusters and applying certain methods to the clustered data can prevent a failure to detect outliers and is computationally efficient due to the reduction of data. In this paper, we suggest to implement a clustering procedure for this purpose and provide examples that illustrate the suggested procedure applied to the Hadi-Simonoff (1993) method, reverse Hadi-Simonoff method, and Gentleman-Wilk (1975) method.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.123-130
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• 2022

Pair trading is a statistical arbitrage investment strategy. Traditionally, cointegration has been utilized in the pair exploring step to discover a pair with a similar price movement. Recently, the clustering analysis has attracted many researchers' attention, replacing the cointegration method. This study tests a clustering-driven pair trading investment strategy in the Korean stock market. If a pair detected through clustering has a large spread during the spread exploring period, the pair is included in the portfolio for backtesting. The profitability of the clustering-driven pair trading strategies is investigated based on various profitability measures such as the distribution of returns, cumulative returns, profitability by period, and sensitivity analysis on different parameters. The backtesting results show that the pair trading investment strategy is valid in the Korean stock market. More interestingly, the clustering-driven portfolio investments show higher performance compared to benchmarks. Note that the hierarchical clustering shows the best portfolio performance.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.225-234
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• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.927-935
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• 2007

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.39-50
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• 1995

This article deals with the problem that all the statistical clustering methods do not supply the clustering rule after the analysis. We modify the Coulomb Energy Network model basically developed in physics and suggest one model appropriate for our purpose and show the implementation using an actual data. Finally the method suggested is compared with one of the well known methods, K-means algorithm using Rand C.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.53-68
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• 1999

To improve the slow convergence property of the steepest ascent type algorithm for continuous D-optimal design problems. we develop a new algorithm. We apply the nonlinear system of equations as the necessary condition of optimality and develop the two-point algorithm that solves the problem of clustering. Because of the nature of the steepest coordinate ascent algorithm avoiding the problem of clustering itself helps the improvement of convergence speed. The numerical examples show the performances of the new method is better than those of various steepest ascent algorithms.