• Korean Journal of Acupuncture
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• v.36 no.4
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• pp.292-299
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• 2019

Objectives : DongUiBoGam is one of the representative medical literatures in Korea. We used text mining methods and analyzed the characteristics of the indications of each meridian in the second chapter of DongUiBoGam, WaeHyeong, which addresses external body elements. We also visualized the relationships between the meridians and the disease sites. Methods : Using the term frequency-inverse document frequency (TF-IDF) method, we quantified values regarding the indications of each meridian according to the frequency of the occurrences of 14 meridians and 14 disease sites. The spatial patterns of the indications of each meridian were visualized on a human body template according to the TF-IDF values. Using hierarchical clustering methods, twelve meridians were clustered into four groups based on the TF-IDF distributions of each meridian. Results : TF-IDF values of each meridian showed different constellation patterns at different disease sites. The spatial patterns of the indications of each meridian were similar to the route of the corresponding meridian. Conclusions : The present study identified spatial patterns between meridians and disease sites. These findings suggest that the constellations of the indications of meridians are primarily associated with the lines of the meridian system. We strongly believe that these findings will further the current understanding of indications of acupoints and meridians.

• Korean Journal of Veterinary Research
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• v.53 no.1
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• pp.49-54
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• 2013

To investigate the transmission pattern of geographical area and temporal trends of the 2010~2011 foot-and-mouth disease (FMD) outbreaks in Korea, and to explore temporal intervals at which spatial clustering of FMD cases space-time analysis based on georeferenced database of 3,575 burial sites, from 30 November 2010 to 23 February 2011, was performed. The cases represent approximately 98.1% of all infected farms (n = 3,644) during the same period. Descriptive maps of spatial patterns of the outbreaks were generated by ArcGIS. Spatial Scan Statistics, using SaTScan software, was applied to investigate geographical clusters of FMD cases across the country. Overall, spatial heterogeneity was identified, and the transmission pattern was different by province. Cattle have more clusters in number but smaller in size, as compared to the swine population. In addition, spatiotemporal analysis and the comparison of clustering patterns between the first 7 days and days 8 to 14 of the outbreak revealed that the strongest spatial clustering was identified at the 7-day interval, although clustering over longer intervals (8~14 days) was also observed. We further discussed the importance of time period elapsed between FMD-suspected notice and the date of confirmation, and emphasized the necessity of region-specific and species-specific control measures.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.143-145
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• 2005

In this paper, an empirical study result on pattern estimation method is devoted to reveal underlying data patterns with a relatively reduced computational cost. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). Conventional SAHN based clustering requires large computation time in the initial step of algorithm. To deal with this concern, we modified overall process with a partial approach. In the beginning of this method, we divide given data set to several sub groups with uniform sampling and then each divided sub data group is applied to SAHN based method. The advantage of this method reduces computation time of original process and gives similar results. Proposed is applied to several test data set and simulation result with conceptual analysis is presented.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.112-119
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• 2001

This paper proposes an adaptive reclosing scheme which is based on the classification of fault patterns. In case that the first reclosing is unsuccessful in distribution system employing with two-shot reclosing scheme, the proposed method can determine whether the second reclosing will be attempted of not. If the first reclosing is unsuccessful two fault currents can be measured before the second reclosing is attempted, where these two fault currents are utilized for an adaptive reclosing scheme. Total harmonic distortion and RMS are used for extracting the characteristics of two fault currents. And the pattern of two fault currents is respectively classified using a mountain clustering method a minimum-distance classifier. Mountain clustering method searches the cluster centers using the acquired past data. And minimum-distance classifier is used for classifying the measured two currents into one of the searched centers respectively. If two currents have the different pattern it is interpreted as temporary fault. But in case of the same pattern, the occurred fault is interpreted as permanent. The proposed method was tested for the fault data which had been measured in KEPCO's distribution system, and the test results can demonstrate the effectiveness of the adaptive reclosing scheme.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• Genomics & Informatics
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• v.10 no.2
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• pp.128-132
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• 2012

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.602-604
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• 2004

A portable electronic tongue(E-Tongue) system using an array of ion-selective electrode(ISE) and personal digital assistants(PDA) for recognizing and analyzing food and drink have been designed. By the employment of PDA, the complex algorithm such as fuzzy c-means algorithm(FCMA) could be used in E-Tongue, FCMA could iteratively solve the cluster centers of pre-determined standard patterns. And the membership between the standard patterns and unknown pattern could be analyzed easily by the present E-Tongue combined with PDA.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.67-73
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• 2012

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study we crawled the keywords from the abstracts in IIE Transactions, one of the representative journals in the field of Industrial Engineering from 1969 to 2011. We applied low-dimensional embedding method, clustering analysis, association rule, and social network analysis to find meaningful associative patterns of key words frequently appeared in the paper.