• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.397-402
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• 2007

In this paper, we present a method for clustering incomplete Microarray data using alternating optimization in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster Information such as cluster centroids and all available non-missing values in each iteration. The clustering results of the proposed method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.

• Journal of the Korean Society of Clothing and Textiles
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• v.10 no.3
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• pp.37-48
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• 1986

The purpose of this study was to analyze 'the natural groupings' of subjects in order to classify highly similar somatotype for clothing construction. The sample for the study was drawn randomly out of senior high school boys in Seoul urban area. The sample size was 425 boys between age 16 and 18. Cluster analysis was more concerned with finding the hierarchical structure of subjects by three dimensional distance of stature. bust girth and sleeve length. The groups forming a partition can be subdivided into 5 and 6 sets by the hierarchical tree of the given subjects. Ward's Minimum Variance Method was applied after extraction of distance matrix by the Standardized Euclidean Distance. All of the above data was analyzed by the computer installed at Korea Advanced Institute of Science and Technology. The major findings, take for instance, of 16 age group can be summarized as follows. The results of cluster analysis of this study: 1. Cluster 1 (32 persons means $18.29\%$ of the total) is characterized with smaller bust girth than that of cluster 5, but stature and sleeve length of the cluster 1 are the largest group. 2. Cluster 2 (18 Persons means $10.29\%$ of the total) is characterized with the group of the smallest stature and sleeve length, but bust girth larger than that of cluster 3. 3. Cluster 3(35persons means $20\%$ of the total) is classified with the smallest group of all the stature, bust girth and sleeve length. 4. Cluster 4(60 persons means $34.29\%$ of the total) is grouped with the same value of sleeve length with the mean value of 16 age group, but the stature and bust girth is smaller than the mean value of this age group. 5. Cluster 5(30 persons means $17.14\%$ of the total) is characterized with smaller stature than that of cluster 1, and with larger bust girth than that of cluster 1, but with the same value of the sleeve length with the mean value of the 16 age group.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.967-973
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• 2017

Clustering is one of the most well-known unsupervised learning methods that clusters data into homogeneous groups. Clustering has been used in various applications and FCM is one of the representative methods. In Fuzzy C-Means(FCM), however, cluster centers tend leaning to high density areas because the Euclidean distance measure forces high density clusters to make more contribution to clustering result. Previously proposed was density-independent clustering method, where cluster centers were made not to be close each other and relived the center deviation problem. Density-independent clustering method has a limitation that it is difficult to specify the position of the cluster centers. In this paper, an enhanced density-independent clustering method with an additional term that makes cluster centers to be placed around dense region is proposed. The proposed method converges more to real centers compared to FCM and density-independent clustering, which can be verified with experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Journal of The Korean Astronomical Society
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• v.38 no.4
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• pp.371-384
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• 2005

Self-referencing method in revised-OTFTOOL is a new method in On-The-Fly(OTF) observation mode. It uses the source free regions of the observed frame as references instead of the OFFs references. We already analyzed and discussed its proprieties and advantages in the previous paper. In this paper, we make a statistical study about the self-referencing method by applying it to OTF mapping data of 27 Virgo spiral galaxies. We found that the self-referencing method solves the crooked baseline problem for every datacube. It straightens the baseline, and conserves the emissions. Compared with other data processing, the median filtering task 'mwflt' in AIPS, to use self-referencing method is more effective and safe not only to straighten the baseline but also to conserve the emission. For the strong CO galaxies, the data obtained by self-referencing method shows scarcely any difference from those reduced by conventional OFFs references and AIPS median filtering in the range of uncertainties. Undetected CO emissions in datacubes of conventional OFFs references are also not detected in those of self-referencing method. The self-referencing method is expected to save the observing time and simplify data reduction processes. Besides this, using self-referencing method will offer emission-free references more safely.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8550-8555
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• 2015

The research goal of this paper is to find the cluster policies for the federal and provincial governments through German Cluster Politics. This paper consists of five chapters. In the first chapter the theoretical background for the Cluster is examined. The next chapter details the research method for the federal and state German Cluster Policy study. The results of this paper show that the federal and state German Governments may co-operate or compete in cluster politics. Additionally, this research falls under EU Political Research under the umbrella of the EU. This cluster policy research also provides implications for the Korean Government in the long term.

• Journal of Korean Clinical Nursing Research
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• v.14 no.1
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• pp.61-70
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• 2008

Purpose: Women with gynecologic cancer often experience various physical and psychological symptoms relating to the cancer and its treatment. The purpose of this study was to identify symptom clusters. Method: A survey was conducted on 184 women with diagnoses of cervical, ovarian or endometrial cancer. Fifty symptoms were assessed for prevalence, severity and interference, and symptom clusters were identified. Cluster analysis was done using SPSS version 12.0. Results: Fatigue was identified as the most prevalent symptom (81.52%), lack of vaginal lubrication (2.26) as the most severe symptom, and lack of vaginal lubrication as the most interfering one (2.15). Identified six clusters were: Anorexia-pain cluster (loss of appetite, taste change, weight loss, appearance change, alopecia, weakness, pain), Fatigue cluster (lack of concentration, lack of memory, fatigue, dry mouth), Urinary-bowel distress cluster (urinary difficulty, constipation), Abdominal discomfort cluster (lower abdominal pain, abdominal pain, bloating), Emotional distress (sadness, anxiety-worry, nervousness, restlessness), and Menopausal cluster (sweating, hot flush, fever). Conclusion: The result of this study provides fundamental data to health care professionals in developing interventions for effective symptom management for women with gynecologic cancer by understanding identified 6 symptom clusters.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.105-113
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• 2009

The previous key management methods are not appropriate for secure data communication in cluster-based routing scheme. Because cluster heads are elected in every round and communicate with the member nodes for authentication and share-key establishment phase in the cluster. In addition, there are not considered to mobility of nodes in previous key management mechanisms. In this paper, we propose the secure and effective key management mechanisim in the cluster-based routing scheme that if there are no share keys between cluster head and its nodes, we create the cluster key using authentication with base station or trust autentication and exchange the their information for a round.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Journal of IKEEE
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• v.27 no.4
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• pp.679-682
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• 2023

In wireless sensor networks, communication failure between sensor nodes causes continuous connection attempts, which results in a large power loss. In this paper, an appropriate distance between the CH(Cluster Head) node and the communicating sensor nodes is limited so that a group of clusters of appropriate size is formed on a two-dimensional plane. To equalize the cluster size, sensor nodes in the shortest distance communicate with each other to form member nodes, and clusters are formed by gathering nearby nodes. Based on the proposed cluster uniformity algorithm, the improvement rate of cluster uniformity is shown by simulation results. The proposed method can improve the cluster uniformity of the network by about 30%.