• Korean Journal of Microbiology
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• v.26 no.3
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• pp.247-255
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• 1988

Using the numerical clustering method, 14 clusters (containing three or more strains) among 231 bacterial isolates from St. 1,17 clusters among 252 isolates from St.2,17 clusters among 301 isolates from St.3, and 15 clusters among 260isolates from St.4 were found at the 70% similarity value. The predominant organisms were identified as genera Aeromonas, Vibrio, and Alcaligenes at St.1, Alcaligenes at St.2, Aeromonas, Vibrio, and Moraxella-Acinetobacter group at St.3, and Pseudomonas at St.4.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.1
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• pp.24-35
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• 2013

A cell formation approach based on cluster analysis is developed for the configuration of manufacturing cells. Cell formation, which is to group machines and parts into machine cells and the associated part families, is implemented to add the flexibility and efficiency to manufacturing systems. In order to develop an efficient clustering procedure, this paper proposes a cluster analysis-based approach developed by incorporating and modifying two cluster analysis methods, a hierarchical clustering and a non-hierarchical clustering method. The objective of the proposed approach is to minimize intercellular movements and maximize the machine utilization within clusters. The proposed approach is tested on the cell formation problems and is compared with other well-known methodologies available in the literature. The result shows that the proposed approach is efficient enough to yield a good quality solution no matter what the difficulty of data sets is, ill or well-structured.

• Women's Health Nursing
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• v.3 no.2
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• pp.208-223
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• 1997

This study was made to suggest the nursing strategies for promoting the behaviors about bone mass health behaviors in order to prevent middle aged women's osteoporosis. This study was a descriptive-correlational design that also concerned to the types which improve bone mass promoting behaviors by inspecting patterns of health locus of control method out of recognizable variables of health improving models influencing on these bone mass promoting behaviors. For these purpose, data were collected by self reported questionnaire in middle school, from 158 women living in Seoul. The measuring tools used in this study about bone mass promoting behaviors and multidimensional health locus of control, were developed by author on the basis of literature review and analyzed by SPSS-PC window, into pearson's correlation, ANOVA, multiple regression, cluster analysis. Data was analyzed as follows. 1. 6 Multidimensional health locus of control scale clusters were existed. : a)cluster I (pure internal), b)cluster II(pure chance), c) cluster III(Believer in control), d), cluster IV(Type VI), e)cluster V(yea sayer), f) cluster VI(nay sayer). There were no findings of the powerful others external cluster and double external cluster. 2. The higher the value of internal health locus of control was, the better the bone mass promoting behaviors were(r=.2891, $p=.00^{**}$). The higher the value of chance external health locus of control was, the worse the bone mass promoting behaviors were(r=-.1367, $p=.00^{**}$). 3. On the basis of these relationships, 6 clusters were significantly different in the bone mass promoting behaviors(F=2.27, $p=.05^*$). The value of bone mass promoting behaviors was ranked the order of type VI>believer in control>pure internal>yea sayer>nay sayer>pure chance external highly. 4. Bone mass promoting behaviors were not significantly different as to age. Suggestion. Based on the results from the study, I would like to make some suggestions as follows. 1) To delay the loss of bone mass in middle aged women, the study on the cluster of the multidimensional health locus of control should be conducted repeatedly. 2) The tool of multidimensional health locus of control should be developed through a qualitative method adjusted on Korean' health culture.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.24-37
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• 2016

This paper proposes to increase the efficiency of energy in nodes, which rapidly drops during the transmission of the Low Energy Adaptive Clustering Hierarchy (LEACH), through the use of dual-hop layered application in the sensor field. Along with introducing the dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission were also introduced. Additionally, by introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was used. In the proposed DL-LEACH, the energy consumption of the cluster head for remote transmission reduced, as well as increased the energy efficiency of the sensor node by reducing the transmission distance and simplifying the transmission route for short-range transmission. As compared the general LEACH, it was adapted to a wider sensor field.

• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.291-298
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• 2010

In wireless sensor networks, energy is the most important consideration because the lifetime of the sensor node is limited by battery. The clustering is the one of methods used to manage network energy consumption efficiently and LEACH(Low-Energy Adaptive Clustering Hierarchy) is one of the most famous clustering algorithms. LEACH utilizes randomized rotation of cluster-head to evenly distribute the energy load among the sensor nodes in the network. The random selection method of cluster-head does not guarantee the number of cluster-heads produced in each round to be equal to expected optimal value. And, the cluster head in a high-density cluster has an overload condition. In this paper, we proposed both a token based cluster-head selection algorithm for guarantee the number of cluster-heads and a cluster selection algorithm for uniform-density cluster. Through simulation, it is shown that the proposed algorithm improve the network lifetime about 9.3% better than LEACH.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1514-1523
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• 2009

It is well known that the current wireless sensor networks addressing methods do not work efficiently in networks more than a few hundred nodes. A standard protocol in ZigBee-Standard feature in ZigBee 2007 gives balanced tree based address assignment method with distributed manner. However, it was limited to cover less than hundreds of sensor nodes due to the wasteful use of available address space, because composed sensor networks usually make an unbalanced tree topology in the real deployment. In this paper, we proposed the hierarchical cluster tree based address assignment method to support large and scalable networks. This method provides unique address for each node with distributed manner and supports hierarchical cluster tree on-demand. Simulation results show that the proposed method reduces orphan nodes due to the address exhaustion and supports larger network with limited address space compared with the ZigBee distributed address assignment method defined in ZigBee-Standard feature in ZigBee 2007.

• The Journal of Bigdata
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• v.5 no.2
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• pp.135-143
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• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.261-269
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• 2007

As a basic research to integrate and to retrieve XML documents efficiently, this paper proposes a clustering method by structures of XML documents. We apply an algorithm processing the many transaction data to the clustering of XML documents, which is a quite different method from the previous algorithms measuring structure similarity. Our method performs the clustering of XML documents not only using the cluster histograms that represent the distribution of items in clusters but also considering the global cluster cohesion. We compare the proposed method with the existing techniques by performing experiments. Experiments show that our method not only creates good quality clusters but also improves the processing time.

• Journal of Environmental Science International
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• v.24 no.2
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• pp.197-205
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• 2015

This study was performed to identify the effect of fertilizer application and planting method on growth and yield of 'Seolhyang' strawberry during seedling raising. According to the concentration of fertilizer applied, the height of daughter plants was the highest at an EC of $0.8dS{\cdot}m^{-1}$. Leaf number and crown diameter were greatest at an EC of $0.6-0.8dS{\cdot}m^{-1}$. In the first measurement, root number was highest in non-fertilizer application, while root weight was heaviest in non-fertilizer application and EC $0.4dS{\cdot}m^{-1}$. The higher the concentration of fertilizer applied as culture media, the lower the growth rate. Thirty days after planting on the main field, plant height and number of new leaf were highest at an EC of $0.8dS{\cdot}m^{-1}$. However, no significant difference was found in leaf length and width and chlorophyll content according to fertilizer application. Marketable yield of 25 g or higher was greatest in EC $0.6dS{\cdot}m^{-1}$. In contrast, no significance was found in total marketable yield at an EC of $0.4-0.8dS{\cdot}m^{-1}$. Aconsistent pattern was exhibited in the growth of 1-5 harvesting flower clusters according to planting method. The length of leaf and flower cluster was short and chlorophyll content was low, when bed soil was removed 100% in harvesting of the first flower cluster. In all treatment, leaf length was shortened until harvesting of the second and third flower cluster, but rapidly lengthened in harvesting of the third and fourth flower clusters. Moreover, the length of flower cluster had a increasing tendency from harvesting of the third flower cluster. However, chlorophyll content was reduced continuously until harvesting of the fifth flower cluster, and was lowest in harvesting of the fourth flower cluster without removal of bed soil. Total yield was greatest in treatment of crown removal in bed soil between November and May. Late marketable yield between March and May was highest in treatment of 100% bed soil removal, followed by treatment of crown removal. Marketable yield of 25 g or higher was greatest in treatment of crown removal between December and February, while greatest in treatment of 100% bed soil removal between March and May.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.461-470
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• 2005

This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. In this case, CRM refers to the strategy of consolidating a company's profits by efficiently managing the clients, providing them with a more effective, personalized service, and managing the resources more effectively. For the realization of a new service policy, this paper analyzes the level of contribution $vis-\acute{a}-vis$ the clients' service pattern (total number of visits to the homepage, service type, service usage period, total payment, average service period, service charge per homepage visit) and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. Likewise, the CRFA (Client Request Filtering algorithm) was suggested per cluster to allocate media server resources. CRFA issues approval within the resource limit of the cluster where the client belongs. In addition, to evaluate the efficiency of CRFA within the Client/Server environment the acceptance rate per class was determined, and an evaluation experiment on network traffic was conducted before and after applying CRFA. The results of the experiments showed that the application of CRFA led to the decrease in network expenses and growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.