• Title/Summary/Keyword: projected clustering

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Structure Preserving Dimensionality Reduction : A Fuzzy Logic Approach

  • Nikhil R. Pal;Gautam K. Nandal;Kumar, Eluri-Vijaya
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.426-431
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    • 1998
  • We propose a fuzzy rule based method for structure preserving dimensionality reduction. This method selects a small representative sample and applies Sammon's method to project it. The input data points are then augmented by the corresponding projected(output) data points. The augmented data set thus obtained is clustered with the fuzzy c-means(FCM) clustering algorithm. Each cluster is then translated into a fuzzy rule for projection. Our rule based system is computationally very efficient compared to Sammon's method and is quite effective to project new points, i.e., it has good predictability.

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Generating FE Mesh Automatically from STL File Model (STL 파일 모델로부터 유한 요소망 자동 생성)

  • Park, Jung-Min;Kwon, Ki-Youn;Lee, Byung-Chai;Chae, Soo-Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.31 no.7 s.262
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    • pp.739-746
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    • 2007
  • Recently, models in STL files are widely used in reverse engineering processes, CAD systems and analysis systems. However the models have poor geometric quality and include only triangles, so the models are not suitable for the finite element analysis. This paper presents a general method that generates finite element mesh from STL file models. Given triangular meshes, the method estimates triangles and makes clusters which consist of triangles. The clusters are merged by some geometric indices. After merging clusters, the method applies plane meshing algorithm, based on domain decomposition method, to each cluster and then the result plane mesh is projected into the original triangular set. Because the algorithm uses general methods to generate plane mesh, we can obtain both tri and quad meshes unlike previous researches. Some mechanical part models are used to show the validity of the proposed method.

Local Projective Display of Multivariate Numerical Data

  • Huh, Myung-Hoe;Lee, Yong-Goo
    • The Korean Journal of Applied Statistics
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    • v.25 no.4
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    • pp.661-668
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    • 2012
  • For displaying multivariate numerical data on a 2D plane by the projection, principal components biplot and the GGobi are two main tools of data visualization. The biplot is very useful for capturing the global shape of the dataset, by representing $n$ observations and $p$ variables simultaneously on a single graph. The GGobi shows a dynamic movie of the images of $n$ observations projected onto a sequence of unit vectors floating on the $p$-dimensional sphere. Even though these two methods are certainly very valuable, there are drawbacks. The biplot is too condensed to describe the detailed parts of the data, and the GGobi is too burdensome for ordinary data analyses. In this paper, "the local projective display(LPD)" is proposed for visualizing multivariate numerical data. Main steps of the LDP are 1) $k$-means clustering of the data into $k$ subsets, 2) drawing $k$ principal components biplots of individual subsets, and 3) sequencing $k$ plots by Hurley's (2004) endlink algorithm for cognitive continuity.

Ab initio calculation of half-metallic ferrocene-based nanowire

  • Kim, Seongmin;Park, Changhwi
    • Proceeding of EDISON Challenge
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    • 2014.03a
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    • pp.425-429
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    • 2014
  • Half-metallic nanostructure is highly applicable in the field of Spintronics and electronic device technology. We examine the electronic properties of a ferrocene-based nanowire as a possible candidate for a half-metallic nanostructure using VASP and SIESTA. Ferrocene-based nanowire shows high stability in both binding energy simulation and molecular dynamics (MD) simulation. The density of states (DOS) and the projected DOS of the ferrocene-based nanowire indicate that one-dimensional clustering of ferrocene molecules can be explained because of p-d orbital hybridization between iron and carbon. Half-metallic property and energy dispersion at the Fermi level due to one-dimensional structure is also observed from the DOS results.

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Development of Core Components of Projected Clustering for High-Dimensional Categorical Data (고차원 범주형 데이터를 위한 투영 군집화 기법의 핵심 요소 개발)

  • Kim Min-Ho;Ramakrishna R.S.
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.181-183
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    • 2006
  • 본 논문은 고차원의 범주형 데이터에 대한 군집화에 대해서 다룬다. 기존의 범주형 데이터 객체를 위한 유사성(상이성) 계측들의 기저에 깔려 있는 한계점은 수치형 데이터에서와 같은 순서화 (ordering)의 부재와 데이터의 고차원성과 희소성에 기인하는데, 이를 효과적으로 극복할 수 있는 기법이 투영 군집화이다. 본 논문에서는 고차원의 범주형 데이터를 효과적으로 처리할 수 있는 투영 군집화를 다루며 핵심 요소인 군집 차원의 정의와 군집 응집도를 제안한다.

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Distribution Analysis of Optimal Equipment Assignment Using a Genetic Algorithm (유전알고리즘을 이용하여 최적화된 방제 자원 배치안의 분포도 분석)

  • Kim, Hye-Jin;Kim, Yong-Hyuk
    • Journal of the Korea Convergence Society
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    • v.11 no.4
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    • pp.11-16
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    • 2020
  • As a plan for oil spill accidents, research to collect and analyze optimal equipment assignments is essential. However, studies that have diversified and analyzed the optimal equipment assignments for responding to oil spill accidents have not been preceded. In response to the need for analyzing optimal equipment assignments study, we devised a genetic algorithm for optimal equipment assignments. The designed genetic algorithm yielded 10,000 optimal equipment assignments. We clustered using the k-means algorithm. As a result, the two clusters of Yeosu, Daesan, and Ulsan, which are expected to be the largest spills, were clearly identified. We also projected 16-dimensional data in two dimensions via Sammon's mapping. The projected data were analyzed for distribution. We confirmed that results of the simulation were better than those of optimal equipment assignments included in the cluster.In the future, it will be possible to implement an approximate model with excellent performance based on this study.

Visualization Method of Document Retrieval Result based on Centers of Clusters (군집 중심 기반 문헌 검색 결과의 시각화)

  • Jee, Tae-Chang;Lee, Hyun-Jin;Lee, Yill-Byung
    • The Journal of the Korea Contents Association
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    • v.7 no.5
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    • pp.16-26
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    • 2007
  • Because it is difficult on existing document retrieval systems to visualize the search result, search results show document titles and short summaries of the parts that include the search keywords. If the result list is long, it is difficult to examine all the documents at once and to find a relation among them. This study uses clustering to classify similar documents into groups to make it easy to grasp the relations among the searched documents. Also, this study proposes a two-level visualization algorithm such that, first, the center of clusters is projected to low-dimensional space by using multi-dimensional scaling to help searchers grasp the relation among clusters at a glance, and second, individual documents are drawn in low-dimensional space based on the center of clusters using the orbital model as a basis to easily confirm similarities among individual documents. This study is tested on the benchmark data and the real data, and it shows that it is possible to visualize search results in real time.

3D Building Reconstruction and Visualization by Clustering Airborne LiDAR Data and Roof Shape Analysis

  • Lee, Dong-Cheon;Jung, Hyung-Sup;Yom, Jae-Hong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.25 no.6_1
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    • pp.507-516
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    • 2007
  • Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

Selecting Representative Views of 3D Objects By Affinity Propagation for Retrieval and Classification (검색과 분류를 위한 친근도 전파 기반 3차원 모델의 특징적 시점 추출 기법)

  • Lee, Soo-Chahn;Park, Sang-Hyun;Yun, Il-Dong;Lee, Sang-Uk
    • Journal of Broadcast Engineering
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    • v.13 no.6
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    • pp.828-837
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    • 2008
  • We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

Discretization of Continuous-Valued Attributes considering Data Distribution (데이터 분포를 고려한 연속 값 속성의 이산화)

  • Lee, Sang-Hoon;Park, Jung-Eun;Oh, Kyung-Whan
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.4
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    • pp.391-396
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    • 2003
  • This paper proposes a new approach that converts continuous-valued attributes to categorical-valued ones considering the distribution of target attributes(classes). In this approach, It can be possible to get optimal interval boundaries by considering the distribution of data itself without any requirements of parameters. For each attributes, the distribution of target attributes is projected to one-dimensional space. And this space is clustered according to the criteria like as the density value of each target attributes and the amount of overlapped areas among each density values of target attributes. Clusters which are made in this ways are based on the probabilities that can predict a target attribute of instances. Therefore it has an interval boundaries that minimize a loss of information of original data. An improved performance of proposed discretization method can be validated using C4.5 algorithm and UCI Machine Learning Data Repository data sets.