• International Journal of Contents
• /
• v.2 no.3
• /
• pp.11-17
• /
• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• Genomics & Informatics
• /
• v.1 no.1
• /
• pp.32-39
• /
• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Communications for Statistical Applications and Methods
• /
• v.27 no.6
• /
• pp.589-602
• /
• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Korean Journal of Community Nutrition
• /
• v.6 no.1
• /
• pp.97-107
• /
• 2001

The clustering of insulin resistance with hypertension, glucose intolerance, hyperinsulinemia, increased triglyceride and decreased HDL cholesterol levels, and central and overall obesity has been called syndrome X, or the insulin resistance syndrome(IRS). To develop a nutrition service for IRS, this study was performed to evaluate the prevalence of each component of the metabolic abnormalities of IRS and analyze the clustering pattern of IRS among subjects living in the Taegu community. Participants in this study were 9234(mean age ; M/F 48/40yrs);63.5% were men, 24.4% were obese, 13.3% had hypertension. 3.7% had hyperglycemia, and 32.4% had hyperlipidemia. The IRS was defined as the coexistence of two or more components among metabolic abnormalities; obesity, hypertension. hyperglucemia and hyperlipidemia. The prevalence of IRS in Taegu was 19.2%(M/F:20.8%/16.4%), the clustering of these fisk variables was higher in advanced age group. Among the subjects of IRS having two of more diseases, 75.6% were obese, the pattern were similar in men and women. The younger, the higher the prevalence of obesity associated clustering patterns. The prevalence of obesity associated patterns among the hyperglycemia associated clustering patterns was 44.5%. The samples of the representative clustering patterns were obesity and hyperlipidemia (8.0%), hypertension and hyperlipidemia(3.2%), hypertension, obesity and hyperlipiemia(3.1%), hypertension and obesity(2.3%), and hyperglycemia and hyperlipidemia(0.8%). The clustering of obesity and hyperlipidemia until 50 year old groups, and the clustering of hypertension and hyperlipidemia in the 60 and 70 age groups were the most prevalent. We concluded that insulin resistance syndrome was a relatively common disorder in the Taegu community, and prevalence and the characteristics of the intervention strategies for IRS are desired, an effective improvement will be achieved.

• The Journal of the Korea Contents Association
• /
• v.2 no.1
• /
• pp.68-76
• /
• 2002

Clustering is representative method of components classification. But, previous clustering method that use cohesion and coupling can not be effective, because design pattern has consisted by relation between classes. In this paper, we classified design patterns with special quality of pattern structure. Classification by clustering had expressed higher correctness degree than classification by facet. Therefore, can do that it is effective that classify design patterns using clustering algorithms that is automatic classification method. When we are searching design patterns, classification of design patterns can compare and analyze similar patterns because similar patterns is saved to same category. Also we can manage repository efficiently because of using and storing link information of patterns.

• Educational Technology International
• /
• v.18 no.2
• /
• pp.159-191
• /
• 2017

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on Learning Management System(LMS) help understanding instructors' pedagogical approach and the integration level of technologies. Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. As a case, we have clustered academic courses based on the usage levels and patterns of LMS in higher education using those three clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and suggest that academic leaders and university staff should look into the usage levels and patterns of LMS with more elaborated view and take an integrated approach with different analytic methods for their strategic decision on development of LMS.

• Smart Structures and Systems
• /
• v.8 no.1
• /
• pp.69-92
• /
• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.10
• /
• pp.410-417
• /
• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.559-563
• /
• 2015

Identification of energy use patterns in buildings has a great opportunity for energy saving. To find what energy use patterns exist, clustering analysis has been commonly used such as K-means and hierarchical clustering method. In case of high dimensional data such as energy use time-series, data reduction should be considered to avoid the curse of dimensionality. Principle Component Analysis, Autocorrelation Function, Discrete Fourier Transform and Discrete Wavelet Transform have been widely used to map the original data into the lower dimensional spaces. However, there still remains an ongoing issue since the performance of clustering analysis is dependent on data type, purpose and application. Therefore, we need to understand which data reduction techniques are suitable for energy use management. This research aims find the best clustering method using energy use data obtained from Seoul National University campus. The results of this research show that most experiments with data reduction techniques have a better performance. Also, the results obtained helps facility managers optimally control energy systems such as HVAC to reduce energy use in buildings.

• The Korean Journal of Applied Statistics
• /
• v.34 no.6
• /
• pp.969-977
• /
• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.