Geometric double-entity model for recognizing far-near relations of clusters
When solving many practical problems, we not only need sample labels given by a clustering algorithm, but also rely on the recognition of far-near relations of clusters. Under the difficult condition of many clusters in a high-dimensional data set, the clustering visualization methods based on dimen...
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| Published in: | Science China. Information sciences Vol. 54; no. 10; pp. 2040 - 2050 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Heidelberg
SP Science China Press
01.10.2011
Springer Nature B.V |
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| ISSN: | 1674-733X, 1869-1919 |
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| Abstract | When solving many practical problems, we not only need sample labels given by a clustering algorithm, but also rely on the recognition of far-near relations of clusters. Under the difficult condition of many clusters in a high-dimensional data set, the clustering visualization methods based on dimension reductions usually produce the phenomena, e.g., some clusters are overlapping, interlacing, or pushed away; as a result, the far-near relations of some clusters are displayed wrongly or cannot be distinguished. The existing inter-cluster distance methods cannot determine whether two clusters are far away or near. The geometric double-entity model method （GDEM） is proposed to describe far-near relations of clusters, and the methods such as the relative border distance, absolute border distance and region dense degree are designed to measure far-near degrees between clusters. GDEM pays attention to both the absolute distance between nearest sample sets and the dense degrees of border regions of two clusters, and it is able to uncover accurately far-near relations of clusters in a high-dimensional space, especially under the difficult condition mentioned above. The experimental results on four real data sets show that the proposed method can effectively recognize far-near relations of clusters, while the conventional methods cannot. |
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| AbstractList | When solving many practical problems, we not only need sample labels given by a clustering algorithm, but also rely on the recognition of far-near relations of clusters. Under the difficult condition of many clusters in a high-dimensional data set, the clustering visualization methods based on dimension reductions usually produce the phenomena, e.g., some clusters are overlapping, interlacing, or pushed away; as a result, the far-near relations of some clusters are displayed wrongly or cannot be distinguished. The existing inter-cluster distance methods cannot determine whether two clusters are far away or near. The geometric double-entity model method (GDEM) is proposed to describe far-near relations of clusters, and the methods such as the relative border distance, absolute border distance and region dense degree are designed to measure far-near degrees between clusters. GDEM pays attention to both the absolute distance between nearest sample sets and the dense degrees of border regions of two clusters, and it is able to uncover accurately far-near relations of clusters in a high-dimensional space, especially under the difficult condition mentioned above. The experimental results on four real data sets show that the proposed method can effectively recognize far-near relations of clusters, while the conventional methods cannot. When solving many practical problems, we not only need sample labels given by a clustering algorithm, but also rely on the recognition of far-near relations of clusters. Under the difficult condition of many clusters in a high-dimensional data set, the clustering visualization methods based on dimension reductions usually produce the phenomena, e.g., some clusters are overlapping, interlacing, or pushed away; as a result, the far-near relations of some clusters are displayed wrongly or cannot be distinguished. The existing inter-cluster distance methods cannot determine whether two clusters are far away or near. The geometric double-entity model method （GDEM） is proposed to describe far-near relations of clusters, and the methods such as the relative border distance, absolute border distance and region dense degree are designed to measure far-near degrees between clusters. GDEM pays attention to both the absolute distance between nearest sample sets and the dense degrees of border regions of two clusters, and it is able to uncover accurately far-near relations of clusters in a high-dimensional space, especially under the difficult condition mentioned above. The experimental results on four real data sets show that the proposed method can effectively recognize far-near relations of clusters, while the conventional methods cannot. |
| Author | WANG KaiJun YAN XuanHui CHEN Li~i |
| AuthorAffiliation | School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350108, China |
| Author_xml | – sequence: 1 givenname: KaiJun surname: Wang fullname: Wang, KaiJun email: wkjwang@gmail.com organization: School of Mathematics and Computer Science, Fujian Normal University – sequence: 2 givenname: XuanHui surname: Yan fullname: Yan, XuanHui organization: School of Mathematics and Computer Science, Fujian Normal University – sequence: 3 givenname: LiFei surname: Chen fullname: Chen, LiFei organization: School of Mathematics and Computer Science, Fujian Normal University |
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| Cites_doi | 10.1186/1471-2105-6-232 10.1007/11494669_93 10.1038/ng765 10.1093/bioinformatics/btg119 10.1016/j.patcog.2008.12.013 10.1109/TPAMI.2007.250607 10.1109/TNN.2008.2000807 10.1109/34.1000238 10.1162/089976698300017953 10.1126/science.286.5439.531 10.1093/bioinformatics/btg025 10.1109/TNN.2005.853574 10.1126/science.1136800 10.1038/nrc2294 10.1360/crad20050912 10.1016/S0165-1684(02)00475-9 10.1109/72.977314 10.1109/TNN.2005.845141 10.1126/science.290.5500.2323 10.1006/geno.2000.6187 10.1016/j.patcog.2008.08.030 10.1145/1015330.1015345 |
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| Copyright | Science China Press and Springer-Verlag Berlin Heidelberg 2011 Science China Press and Springer-Verlag Berlin Heidelberg 2011. |
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| Keywords | partitional clustering algorithms geometric double-entity model far-near relations of clusters distance between clusters |
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| Notes | 11-5847/TP geometric double-entity model, far-near relations of clusters, distance between clusters, partitionalclustering algorithms When solving many practical problems, we not only need sample labels given by a clustering algorithm, but also rely on the recognition of far-near relations of clusters. Under the difficult condition of many clusters in a high-dimensional data set, the clustering visualization methods based on dimension reductions usually produce the phenomena, e.g., some clusters are overlapping, interlacing, or pushed away; as a result, the far-near relations of some clusters are displayed wrongly or cannot be distinguished. The existing inter-cluster distance methods cannot determine whether two clusters are far away or near. The geometric double-entity model method （GDEM） is proposed to describe far-near relations of clusters, and the methods such as the relative border distance, absolute border distance and region dense degree are designed to measure far-near degrees between clusters. GDEM pays attention to both the absolute distance between nearest sample sets and the dense degrees of border regions of two clusters, and it is able to uncover accurately far-near relations of clusters in a high-dimensional space, especially under the difficult condition mentioned above. The experimental results on four real data sets show that the proposed method can effectively recognize far-near relations of clusters, while the conventional methods cannot. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| Title | Geometric double-entity model for recognizing far-near relations of clusters |
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