• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.1-8
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• 2006

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.426-436
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• 2000

Given a linear arrangement of n base stations which generate multiple types of traffic among themselves, we consider the problem of finding a set of disjoint clusters to cover n base statons so that a cluster is assigned a location server. Our goal is to minimize the total communication cost for the entire network where the cost of intra-cluster communication is usually lower than that of intercluster communication for each type of traffic. The optimization problem is transformed into an equivavalent problem using the concept of relative cost, which generates the difference of communication costs between intracluster and intercluster communications. Using the relative cost matrix, an efficient algorithm of O($mm^2$), where m is the number of clusters in a partition, is designed by dynamic programming. The algorithm also finds all thevalid partitions in the same polynomial time, given the size constraint on a cluster, and the total allowable communication cost for the entire network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.65
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• pp.41-50
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• 2001

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and intercluster similarity is minimized. The discovered clusters from clustering process are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related transactions with categorical attributes. Our approach starts with transforming general relational databases into a transactional databases. We make use of inter-dimensional association rules for composing hypergraph edges, and a hypergraph partitioning algorithm for clustering the values of attributes. The clusters of the values of attributes are used to find the clusters of transactions. The suggested procedure can enhance the interpretation of resulting clusters with allocated attribute values.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.96-106
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• 2009

Extending the scale of the distributed processing in a single Ethernet cluster is physically restricted by maximum ports per switch. This paper presents an implementation of MPI-based multicluster consisting of multiple Ethernet switches for extending the scale of distributed processing, and a asymptotical analysis for communication overhead through execution-time analysis model. To determine an optimum task partitioning, we analyzed the processing time for various partitioning schemes, and AAP(accumulator array partitioning) scheme was finally chosen to minimize the overall communication overhead. The scope of data partitioned in AAP was modified to fit for incremented nodes, and suitable load balancing algorithm was implemented. We tried to alleviate the communication overhead through exploiting the pipelined broadcast and flat-tree based result gathering, and overlapping of the communication and the computation time. We used the linear pipeline broadcast to reduce the communication overhead in intercluster which is interconnected by a single link. Experimental results shows nearly linear speedup by the proposed parallel distributed GHT implemented on MPI-based Ethernet multicluster with four 100Mbps Ethernet switches and up to 128 nodes of Pentium PC.

• Bulletin of the Korean Chemical Society
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• v.28 no.7
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• pp.1097-1103
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• 2007

We report a novel method to synthesize nanocomposites composed of titania nanoparticles and phosphotungstate ions with various composition ratios ranging from W/Ti = 12/10 to 12/500 by inducing the electrostatic interaction between the positively charged protonated titania sol-particles and the negatively charged phosphotungstate anions to flocculate and precipitate. The precipitates showed varied features depending on the composition. The precipitate from the tungsten-richest W/Ti = 12/10 reaction is amorphous in its powder X-ray diffraction (XRD), transmission electron microscopy (TEM), and Raman spectroscopy data. This material shows the Type II adsorption characteristics in its N2-adsorption isotherm, but with quite low surface area of 34 m2/g. To the contrary, the precipitates from the titanium-richer reactions (W/Ti = 12/50- 12/500) are composed of anatase nanoparticles of 2-6 nm by XRD, TEM and Raman and show the Type I adsorption characteristics. The surface area linearly increases with the titanium content from 131 m2/g for W/ Ti = 12/50 to 228 m2/g for 12/500. The precipitate from the reaction with the intermediate composition W/Ti = 12/20 is composed of anatase nanoparticles and does not have any pore accessible to N2. With the wide variety of the physical properties of the precipitates, the present method can be a novel, viable means to tailor synthesis of nanocomposite materials. A formation mechanism of the precipitates is based on the electrostatic interactions between the titania nanoparticles and phosphotungstate ions.

• Journal of The Korean Astronomical Society
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• v.35 no.3
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• pp.111-121
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• 2002

We have measured the correlation functions of the optically selected clusters of galaxies in the Abell and the APM catalogs, and of the X-ray clusters in the X-ray-Brightest Abell-type Clusters of galaxies (XBACs) catalog and the Brightest Clusters Sample (BCS). The same analysis method and the same method of characterizing the resulting correlation functions are applied to all observational samples. We have found that the amplitude of the correlation function of the APM clusters is much higher than what has been previously claimed, in particular for richer subsamples. The correlation length of the APM clusters with the richness R $\ge$ 70 (as defined by the APM team) is found to be $r_0 = 25.4_{-3.0}^{+3.1}\;h^{-1}$ Mpc. The amplitude of correlation function is about 2.4 times higher than that of Croft et al. (1997). The correlation lengths of the Abell clusters with the richness class RC $\ge$ 0 and 1 are measured to be $r_0 = 17.4_{-1.1}^{+1.2}$ and $21.0_{-2.8}^{+2.8}\;h^{-1}$ Mpc, respectively, which is consistent with our results for the APM sample at the similar level of richness. The richness dependence of cluster correlations is found to be $r_0= 0.40d_c + 3.2$ where $d_c$ is the mean intercluster separation. This is identical in slope with the Bahcall & West (1992)'s estimate, but is inconsistent with the weak dependence of Croft et al. (1997). The X-ray bright Abell clusters in the XBACs catalog and the X-ray selected clusters in the BCS catalog show strong clustering. The correlation length of the XBACs clusters with $L_x {\ge}0.65{\times} 10^{44}\;h^{-2}erg\;s^{-1}$ is $30.3_{-6.5}^{+8.2}\;h^{-1}$ Mpc, and that of the BCS clusters with $L_x {\ge}0.70{\times} 10^{44}\;h^{-2}erg\;s^{-1}$ is $30.2_{-8.9}^{+9.8}\;h^{-1}$ Mpc. The clustering strength of the X-ray clusters is much weaker than what is expected from the optical clusters.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.47-60
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• 2007

The purpose of this study is to compare and to evaluate the effect of pH and lactic acid concentration on the progression of artificial root caries lesion using polarizing microscope, and to evaluate the morphological changes of hydroxyapatite crystals of the demineralized area and to investigate the process of demineralization using scanning electron microscope. Artificial root caries lesion was created by dividing specimens into 3 pH groups (pH 4.3, 5.0, 5.5), and each pH group was divided into 3 lactic acid concentration groups (25 mM, 50 mM, 100 mM). Each group was immersed in acid buffer solution for 5 days and examined. The results were as follows : 1. Under polarized microscope, the depth of lesion was more effected by the lactic acid concentration rather than the pH. 2. Under scanning electron microscope, dissolution of hydroxyapatite crystals were increased as the lactic acid concentration increased and the pH decreased. 3. Demineralized hydroxyapatite crystals showed peripheral dissolution and decreased size and number within cluster of hydroxyapatite crystals and widening of intercluster and intercrystal spaces as the pH decreased and the lactic acid concentration increased. 4. Under scanning electron microscope evaluation of the surface zone, clusters of hydroxyapatite crystals were dissolved, and dissolution and reattachment of crystals on the surface of collagen fibrils were observed as the lactic acid concentration increased. 5. Under scanning electron microscope, demineralizatlon of dentin occurred not only independently but also with remineralization simultaneously. In conclusion, the study showed that pH and lactic acid concentration influenced the rate of progression of the lesion in artificial root caries. Demineralization process was progressed from the surface of the cluster of hydroxyapatite crystals and the morphology of hydroxyapatite crystals changed from round or elliptical shape into irregular shape as time elapsed.