• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1147-1161
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• 2015

Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.

• 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 Society of Clothing and Textiles
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• v.22 no.6
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• pp.781-792
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• 1998

The purpose of this study was to investigate the factors related to the propensity for symbolic consumption and the effects of materialism, reference group, and social stratification on the symbolic consumption in clothing. Data were administered to 957 adolescence in middle, high school, and college student living in Seoul, Chonju, Sunchon, Yousu, and Kwangyang from May to June 1997. For analysis of the data, frequencies, percentage, means, standard deviation, factor analysis, 1-test, one-way anomia, duncan's multiple range test, and multiple regression analysis were employed. The results of this study can be summarized asfollows. 1) Symbolic consumption, materialism, and reference group were found to have the significant differences according to social stratification groups by objectivemethod. The higher social stratification is, the higher symbolic consumption, materialism, and reference group were. 2) symbolic consumption were proven to have the significant differences according to materiaiism and reference group. The higher materialism and the influence of referencegroup indicated, the higher symbolic consumption. 3)according to the results of the regression analysis examining the relative influences of variables affecting symbolic consumption in clothing, the relative importance of the variables are in order of : the influences of the reference group, materialism, social stratification, status inconsistency type (occupation-income), and their explanatory power totalled 40.0%.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.481-488
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• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of the Korean Home Economics Association
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• v.36 no.10
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• pp.131-144
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• 1998

The purpose of this study was to examine the differences of symbolic consumption of adolescents, and the effects of demographic factors on the symbokic consumption in clothing. Data were administered to 957 adolescents in middle, high school, and college students living in Seoul, Chonju, Sunchon, Yousu, and Kwangyang from May to June 1997. For analysis of the data, factor analysis, t-test, one-way ANOCA, duncan's multiple range test, and multiple regression analysis were employed. The results of this study were summarized as follows. 1) Symbolic consumption in colthing were shown to have the significant differences accoding to age, gender, the level of urbanization, parent's education, father's occupation, social stratification groups. The higher the age, the level of urbanization, and parent's education, father's occupation, social stratification is, or the female, the higher is symbolic consumption in clothing. 2) According to the results of the regression analysis examining the rerlative influences of variables affecting symbolic consumption in clothing, the relative importance of the variables are in order of; income, gender, age, mother's education, residence, and their explanatory powere totalled 11.5%.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.471-486
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• 2014

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.729-734
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• 2012

Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.6
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• pp.247-259
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• 1996

Kinematic design sensitivity analysis for vehicle in suspension systems design is performed. Suspension systems are modeled using composite joins to reduce the number of the constraint equations. This allows a semi-analytical approach that is computerized symbolic manipulation before numerical computations and that may compensate for their drawbacks. All the constraint equations including design variables are derived in symbolic equations for sensitivity analysis. By directly differentiating the equations with respect to design variables, sensitivity equations are obtained. Since the proposed method only requires the hard point data, sensitivity analysis is possible in suspension design stage.

• Fashion & Textile Research Journal
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• v.10 no.4
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• pp.499-505
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• 2008

The Purpose of this study is to research the relations between brand attachment and brand loyalty depending on symbolic consumption propensity toward fashion goods. Subjects were 391 women in their twenties to fifties who live in Gyungsang Province and have purchased the fashion goods. Frequency analysis, reliability analysis, factor analysis, multiple regression analysis, and one-way layout variance analysis were conducted using SPSS 13.0 as data analysis. The findings from the analysis are described in the following: Uniqueness and materialism out of the symbolic consumption propensity toward fashion goods had positive effects on the elements of brand attachment such as love, care, and knowledge. Brand loyalty was influenced positively by social face sensitivity and materialism among symbolic consumption toward fashion goods.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1239-1247
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• 2015

In this study, 64 administrative regions with high frequencies of apartment trade in Seoul, Korea are classified by the apartment sale price. To consider distributions of apartment price for each region as well as the mean of the price, the symbolic histogram-valued data approach is employed. Symbolic data include all types of data which have internal variation in themselves such as intervals, lists, histograms, distributions, and models, etc. As a result of the cluster analysis using symbolic histogram data, it is found that Gangnam, Seocho, and Songpa districts and regions near by those districts have relatively higher prices and larger dispersions. This result makes sense because those regions have good accessibility to downtown and educational environment.