• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.387-397
• /
• 2003

Generally, most of researches that need a variable-clustering process use an exploratory factor analysis technique or a divisive hierarchical variable-clustering method based on a correlation matrix. And some researchers apply a object-clustering method to a distance matrix transformed from a correlation matrix, though this approach is known to be improper. On this paper an agglomerative hierarchical variable-clustering method based on a correlation matrix itself is suggested. It is derived from a geometric concept by using variate-spaces and a characterizing variate.

• Communications for Statistical Applications and Methods
• /
• 제11권2호
• /
• pp.369-380
• /
• 2004

Clustering algorithms with different similarity measures are commonly used to find an optimal clustering or close to original clustering. The recovery level of using Euclidean distance and distances transformed from correlation coefficients is evaluated and compared using Rand's (1971) C statistic. The C values present how the resultant clustering is close to the original clustering. In simulation study, the recovery level is improved by applying the correlation coefficients between objects. Using the data set from Spellman et al. (1998), the recovery levels with different similarity measures are also presented. In general, the recovery level of true clusters was increased by using the correlation coefficients.

• IEIE Transactions on Smart Processing and Computing
• /
• 제5권2호
• /
• pp.85-93
• /
• 2016

The existence of correlation characteristics brings significant potential advantages to the development of efficient routing protocols in wireless sensor networks. This research proposes a new simple method of clustering sensor nodes into correlation groups in multiple-correlation areas. At first, the evaluation of joint entropy for multiple-sensed data is considered. Based on the evaluation, the definition of correlation region, based on entropy theory, is proposed. Following that, a correlation clustering scheme with less computation is developed. The results are validated with a real data set.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제3권3호
• /
• pp.331-343
• /
• 2009

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.

• 산업기술연구
• /
• 제29권B호
• /
• pp.17-23
• /
• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
• /
• pp.601-604
• /
• 2000

In this paper, we compare the classification performance of both data fusion and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. Since the relationship between input & output is not typically known, we use Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: Clustering based logistic regression turns out to provide the highest classification accuracy when input variables are weakly correlated and the variance of data is high. When there is high correlation among input variables, variable bagging performs better than logistic regression. When there is strong correlation among input variables and high variance between observations, bagging appears to be marginally better than logistic regression but was not significant.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
• /
• pp.111-114
• /
• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• 대한산업공학회지
• /
• 제27권1호
• /
• pp.47-53
• /
• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• 한국정보통신학회논문지
• /
• 제26권5호
• /
• pp.755-762
• /
• 2022

본 논문에서는 학습 예측이 가능한 군집적 알고리즘으로 COVID-19에서 상황인식정보인 질병의 속성정보와 클러스터링를 이용한 군집적 알고리즘을 제안한다. 클러스터링 내에서 처리되는 군집 데이터는 신규 또는 새롭게 입력되는 정보가 상호관계를 예측하기 위해 분류 제공되는데, 이때 새롭게 입력되는 정보가 비교정보에서 오염된 정보로 처리되면 기존 분류된 군집으로부터 벗어나게 되어 군집성을 저하시키는 요인으로 작용하게 된다. 본 논문에서는 COVID-19에서의 질병속성 정보내 K-means알고리즘을 이용함에 있어 이러한 문제를 해결하기 위해 질병 상호관계 정보 추출이 가능한 사용자 군집 분석 방식을 제안하고자 한다. 제안하는 알고리즘은 자율적인 사용자 군집 특징의 상호관계를 분석학습하고 이를 통하여 사용자 질병속성간에 따른 클러스터를 구성해 사용자의 누적 정보로부터 클러스터의 중심점을 제공하게 된다. 논문에서 제안된 COVID-19의 다중질병 속성정보군집단위로 분류하고 학습하는 알고리즘은 적용한 모의실험 결과를 통해 사용자 관리 시스템의 예측정확도가 학습과정에서 향상됨을 보여주었다.

• 천문학논총
• /
• 제27권4호
• /
• pp.343-344
• /
• 2012

We present the first measurement of the angular two-point correlation function for AKARI $90{\mu}m$ point sources, detected outside of the Milky Way plane and selected as candidates for extragalactic sources. This is the first measurement of the large-scale angular clustering of galaxies selected in the far-infrared after IRAS. We find a positive clustering signal in both hemispheres extending up to ~ 40 degrees, without any significant fluctuations at larger scales. The observed correlation function is well fitted by a power law function. However, southern galaxies seem to be more strongly clustered than northern ones and the difference is statistically significant. The reason for this difference - technical or physical - is still to be found.