• Title/Summary/Keyword: Analysis of means

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Program Development of Integrated Expression Profile Analysis System for DNA Chip Data Analysis (DNA칩 데이터 분석을 위한 유전자발연 통합분석 프로그램의 개발)

  • 양영렬;허철구
    • KSBB Journal
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    • v.16 no.4
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    • pp.381-388
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    • 2001
  • A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

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Improvement on Fuzzy C-Means Using Principal Component Analysis

  • Choi, Hang-Suk;Cha, Kyung-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.301-309
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    • 2006
  • In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

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K-means Clustering using Grid-based Representatives

  • Park, Hee-Chang;Lee, Sun-Myung
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.759-768
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    • 2005
  • K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

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An Analysis of the Type of Attitudes Toward Money and Expenditure Behavior (도시가계의 화폐태도유형과 지출행동분석)

  • 백은영
    • Journal of the Korean Home Economics Association
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    • v.36 no.3
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    • pp.47-60
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    • 1998
  • This Pupose of this study was to identify the type of attitudes toward money to investigate the relationship between the attitued and consumption expenditure pattern. Data were obtained from 398 household living in Seoul. Factor analysis was used for examining dimensions of attitudes toward money and cluster analysis for classifying the households by money attitudes. This study found five money attitude dimensions, i.e., the Means of Success dimension, the Means of Pleasure dimension, the Means of Security dimension, the Symbol of Anxiety dimension, and the Parsimony dimension, Based on the variation in the dimensions, five money types were identified, ie., the Means of Success, the Means of Pleasure, the Means of Security, the Symbol of Anxiety, and the Parsimony.

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Classification Tree-Based Feature-Selective Clustering Analysis: Case of Credit Card Customer Segmentation (분류나무를 활용한 군집분석의 입력특성 선택: 신용카드 고객세분화 사례)

  • Yoon Hanseong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.4
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    • pp.1-11
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    • 2023
  • Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.

Probabilistic reduced K-means cluster analysis (확률적 reduced K-means 군집분석)

  • Lee, Seunghoon;Song, Juwon
    • The Korean Journal of Applied Statistics
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    • v.34 no.6
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    • pp.905-922
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    • 2021
  • Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

Automatic Extraction of Blood Flow Area in Brachial Artery for Suspicious Hypertension Patients from Color Doppler Sonography with Fuzzy C-Means Clustering

  • Kim, Kwang Baek;Song, Doo Heon;Yun, Sang-Seok
    • Journal of information and communication convergence engineering
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    • v.16 no.4
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    • pp.258-263
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    • 2018
  • Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

Analysis of Brokerage Commission Policy based on the Potential Customer Value (고객의 잠재가치에 기반한 증권사 수수료 정책 연구)

  • Shin, Hyung-Won;Sohn, So-Young
    • IE interfaces
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    • v.16 no.spc
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    • pp.123-126
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    • 2003
  • In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

Application of Analysis of Means(ANOM) for Design of Experiment (실험계획법에서 평균분석(ANOM)의 응용)

  • Choi, Sung-Woon
    • Proceedings of the Safety Management and Science Conference
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    • 2008.04a
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    • pp.283-293
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    • 2008
  • Analysis of Means(ANOM) is a visualization tool for comparing several means to the grand mean like control chart type. This paper reviews five ANOM methods for continuous data such as ANOM, ANOME (ANOM for Treatment Effects), ANCON (Analysis of Contrasts), ANOMV (ANOM for Variance), ANOMC (ANOM for Correaltion). Three ANOM tools for discrete data such as ANOMNP (ANOM for Nonconforming Proportions), ANOMNC (ANOM for Nonconforming Unit), ANOMNPU (ANOM for Nonconfirmities Per Unit) are also developed.

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Analysis of Combined Yeast Cell Cycle Data by Using the Integrated Analysis Program for DNA chip (DNA chip 통합분석 프로그램을 이용한 효모의 세포주기 유전자 발현 통합 데이터의 분석)

  • 양영렬;허철구
    • KSBB Journal
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    • v.16 no.6
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    • pp.538-546
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    • 2001
  • An integrated data analysis program for DNA chip containing normalization, FDM analysis, various kinds of clustering methods, PCA, and SVD was applied to analyze combined yeast cell cycle data. This paper includes both comparisons of some clustering algorithms such as K-means, SOM and furry c-means and their results. For further analysis, clustering results from the integrated analysis program was used for function assignments to each cluster and for motif analysis. These results show an integrated analysis view on DNA chip data.

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