• Journal of Korea Spatial Information System Society
• /
• v.7 no.3 s.15
• /
• pp.33-44
• /
• 2005

Recently, the weather has been influenced by the environmental pollution and the oil price has been risen because of the lack of resources. So, the weather and energy are influencing on not only enterprises or nations, but also individual daily life and economic activities very much. Because of these reasons, there are so many researches about management of solar radiation needed to develope solar energy as alternative energy. And many researchers are also interested in identifying the area according to changing characteristics of climate data. However, the researches have not developed how to apply the cluster analysis, retrieval and analytical results according to the characteristics of the area through data mining. In this paper, we design a data model of the data for storing and managing the climate data tested in twenty cities in the domestic area. And we provide the information according to the characteristics of the area after clustering the domestic climate data, using k-means clustering algorithm. And we suggest the way how to apply the department store and amusement park as an applied weather marketing. The proposed system is useful for constructing the database about the weather marketing and for providing the elements and analysis information.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.2
• /
• pp.139-147
• /
• 2009

BLAST (Basic Local Alignment Search Tool) is a best well-known tool in a bioinformatics area. BLAST quickly compares input sequences with annotated huge sequence databases and predicts their functions. It helps biologists to make it easy to annotate newly found sequences with reduced experimental time, scope, and cost. However, as the amount of sequences is increasing remarkably with the advance of sequencing machines, performance of BLAST has been a critical issue and tried to solve it with several alternatives. In this paper, we propose a new PC-Based Cluster system (E-Cluster), a new physical data design methodology (logical partitioning technique) and a query routing technique (intra-query routing). To verify our system, we measure response time, speedup, and efficiency for various sizes of sequences in NR (Non-Redundancy) database. Experimental result shows that proposed system has better speedup and efficiency (maximum 600%) than those o( conventional approaches such as SMF machines, clusters, and grids.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.399-400
• /
• 2009

As a part of the era of human centric applications people started to care about their well being utilizing any possible mean. This paper proposes a framework for real time on-body sensor health-care system, addresses the current issues in such systems, and utilizes an enhanced online divisive agglomerative clustering algorithm (EODAC); an algorithm that builds a top-down tree-like structure of clusters that evolves with streaming data to rationally cluster on-body sensor data and give accurate diagnoses remotely, guaranteeing high performance, and scalability. Furthermore it does not depend on the number of data points.

• Genomics & Informatics
• /
• v.4 no.4
• /
• pp.173-181
• /
• 2006

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06c
• /
• pp.26-30
• /
• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.05a
• /
• pp.231-234
• /
• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• Fashion & Textile Research Journal
• /
• v.23 no.5
• /
• pp.667-673
• /
• 2021

The purpose of this study was to obtain drape information and objective texture of fabrics easily and quickly by using a constructed fabric database. For the construction of the fabric database, 287 woven fabrics were examined by using the CLO fabric kit, KES-FB system, and drape test system. The k-means cluster analysis method was used to classify the fabrics into 7 grades. After correlation analysis of the fabric properties for each experiment, similar properties of the CLO fabric kit and KES-FB system were chosen, which were then designed to extract similar fabrics from the database. It was confirmed that inferring the drape information and objective hand feeling of fabrics was to some extent possible by extracting similar fabrics from the database. In this study, the primary hand and total hand value(THV) of KES-FB system, which was constructed by Kawabata and other experiments, were used to quantify the objective hand feeling, because they are the most widely used. However, these standards can be changed over time; in order to be applied within the clothing industry, these standards may have to be changed to some extent. Moreover, it is notable that although objective hand feeling cannot be expressed in the 3D virtual costume program, it can be easily derived from the constructed database. Additionally, it is expected that the existing 3D virtual costume program will express the costumes more realistically by improving these results.

• The KIPS Transactions:PartB
• /
• v.16B no.2
• /
• pp.165-172
• /
• 2009

According to the American Cancer Society, breast cancer is the second largest cause of cancer deaths and most frequently diagnosed cancer in women. The currently most popular method for early detection of breast cancer is the digital mammography. A mass or calcification lesion has been known as the most important clue for the diagnosis. In this paper, we propose a diagnosis approach based on fuzzy cluster knowledge base. We combine different two sources of feature data in duel OFUN-NET and produce the diagnosis result with possibility degree. We also present the experimental results on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. These results show higher classification accuracy than conventional methods and the feasibility as a decision supporting tool for diagnosis of digital mammogram.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.28 no.910
• /
• pp.1300-1311
• /
• 2004

The aim of this study was to provide the 2 and 3 dimensional statistics requisite in the sizing system and design of gloves. The 64 2-dimensional static measurements were selected to provide information about hands. Participants in the study were 824 adults, aged between 18 and 64. To summarize the information from the measurement values, a Factor Analysis and a Cluster Analysis among multivariate analyses were performed. 3-D scanner was used for visual results of hand shape of each cluster. The results were as follows. Twenty-two items were used for the factor and cluster analysis in order to classify the adult hand shape. The variable quantities that are explained by a total of 3 factors amounted to under 79.37% of the variable quantities. The definition results of the factors related to the hands are as follows: Factor 1 is the horizontal dimension, the thickness of hand factor; Factor 2 is the height of the crotch; and Factor 3 is the vertical dimension of the hand. The adults' group hand was divided into 2 clusters according to a cluster analysis using factor scores. The characteristics according to hand type were as follows: Cluster 1 referred to high horizontal dimensions and thickness, rather small vertical dimensions and crotch height; and Cluster 2 represented the rather smaller horizontal dimensions and thickness but longer hand length than Type 1. To provide specific shape data of each cluster, 3-D scanner measurement was performed. 3-dimensional data base was developed for each cluster type and visual information was provided.

• Journal of KIISE:Databases
• /
• v.30 no.6
• /
• pp.629-640
• /
• 2003

A shared disk (SD) cluster couples multiple nodes for high performance transaction processing, and all the coupled nodes share a common database at the disk level. In the SD cluster, a transaction routing corresponds to select a node for an incoming transaction to be executed. An affinity-based routing can increase local buffer hit ratio of each node by clustering transactions referencing similar data to be executed on the same node. However, the affinity-based routing is very much non-adaptive to the changes in the system load, and thus a specific node will be overloaded if transactions in some class are congested. In this paper, we propose a dynamic transaction routing scheme that can achieve an optimal balance between affinity-based routing and dynamic load balancing of all the nodes in the SD cluster. The proposed scheme is novel in the sense that it can improve the system performance by increasing the local buffer hit ratio and reducing the buffer invalidation overhead.