A Novel Density-Based Clustering Framework by Using Level Set Method
In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundar...
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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 21; číslo 11; s. 1515 - 1531 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.11.2009
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1041-4347, 1558-2191 |
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| Abstract | In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundary formation scheme. Accordingly, three types of initial boundaries are defined so that each of them can evolve to approach the cluster centers in different ways. To avoid the long iteration time of level set evolution in data space, an efficient termination criterion is presented to stop the evolution process in the circumstance that no more cluster centers can be found. Then, a new effective density representation called level set density (LSD) is constructed from the evolution results. Finally, the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. The experiments on some synthetic and real data sets have demonstrated the efficiency and effectiveness of the proposed clustering framework. The comparisons with DBSCAN method, OPTICS method, and valley seeking clustering method further show that the proposed framework can successfully avoid the overfitting phenomenon and solve the confusion problem of cluster boundary points and outliers. |
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| AbstractList | In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundary formation scheme. Accordingly, three types of initial boundaries are defined so that each of them can evolve to approach the cluster centers in different ways. To avoid the long iteration time of level set evolution in data space, an efficient termination criterion is presented to stop the evolution process in the circumstance that no more cluster centers can be found. Then, a new effective density representation called level set density (LSD) is constructed from the evolution results. Finally, the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. The experiments on some synthetic and real data sets have demonstrated the efficiency and effectiveness of the proposed clustering framework. The comparisons with DBSCAN method, OPTICS method, and valley seeking clustering method further show that the proposed framework can successfully avoid the overfitting phenomenon and solve the confusion problem of cluster boundary points and outliers. [...] the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. |
| Author | Xiao-Feng Wang De-Shuang Huang |
| Author_xml | – sequence: 1 givenname: Xiao-Feng surname: WANG fullname: WANG, Xiao-Feng organization: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, PO Box 1130, Hefei Anhui 230031, China – sequence: 2 givenname: De-Shuang surname: HUANG fullname: HUANG, De-Shuang organization: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, PO Box 1130, Hefei Anhui 230031, China |
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| Snippet | In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target... [...] the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. |
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| SubjectTerms | Applied sciences Artificial intelligence Boundaries Clustering Clustering algorithms Clustering methods Clusters Computer science; control theory; systems Computer systems and distributed systems. User interface Costs Data mining Data processing. List processing. Character string processing Density Density-based clustering Evolution Exact sciences and technology Image retrieval Image segmentation Information retrieval initial boundary Labeling Level set level set density level set method LSD Memory organisation. Data processing Pattern classification Pattern recognition. Digital image processing. Computational geometry Software Studies valley seeking clustering Valleys |
| Title | A Novel Density-Based Clustering Framework by Using Level Set Method |
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