An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity
Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity matrix, so they are sensitive to the selectio...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8766800/ |
| _version_ | 1818849201201610752 |
|---|---|
| author | Lijuan Wang Shifei Ding Hongjie Jia |
| author_facet | Lijuan Wang Shifei Ding Hongjie Jia |
| author_sort | Lijuan Wang |
| collection | DOAJ |
| description | Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity matrix, so they are sensitive to the selection of scale parameter. In addition, they need to randomly determine the initial cluster centers at the clustering stage and the clustering performance is not stable. Therefore, this paper presents an algorithm on the basis of message passing, which makes use of a density adaptive similarity measure, describing the relations between data points and obtaining high-quality cluster centers through message passing mechanism in AP clustering. The performance of clustering is optimized by this method. The experiments show that the proposed algorithm can effectively deal with the clustering problem of multi-scale datasets. Moreover, its clustering performance is very stable, and the clustering quality is better than traditional spectral clustering algorithm and k-means algorithm. |
| first_indexed | 2024-12-19T06:29:28Z |
| format | Article |
| id | doaj.art-f453ba1b252b473a808421a38e3bb6a9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T06:29:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f453ba1b252b473a808421a38e3bb6a92022-12-21T20:32:26ZengIEEEIEEE Access2169-35362019-01-01710105410106210.1109/ACCESS.2019.29299488766800An Improvement of Spectral Clustering via Message Passing and Density Sensitive SimilarityLijuan Wang0Shifei Ding1https://orcid.org/0000-0002-1391-2717Hongjie Jia2School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSpectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity matrix, so they are sensitive to the selection of scale parameter. In addition, they need to randomly determine the initial cluster centers at the clustering stage and the clustering performance is not stable. Therefore, this paper presents an algorithm on the basis of message passing, which makes use of a density adaptive similarity measure, describing the relations between data points and obtaining high-quality cluster centers through message passing mechanism in AP clustering. The performance of clustering is optimized by this method. The experiments show that the proposed algorithm can effectively deal with the clustering problem of multi-scale datasets. Moreover, its clustering performance is very stable, and the clustering quality is better than traditional spectral clustering algorithm and k-means algorithm.https://ieeexplore.ieee.org/document/8766800/Spectral clusteringsimilarity matrixmessage passingclustering stability |
| spellingShingle | Lijuan Wang Shifei Ding Hongjie Jia An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity IEEE Access Spectral clustering similarity matrix message passing clustering stability |
| title | An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity |
| title_full | An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity |
| title_fullStr | An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity |
| title_full_unstemmed | An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity |
| title_short | An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity |
| title_sort | improvement of spectral clustering via message passing and density sensitive similarity |
| topic | Spectral clustering similarity matrix message passing clustering stability |
| url | https://ieeexplore.ieee.org/document/8766800/ |
| work_keys_str_mv | AT lijuanwang animprovementofspectralclusteringviamessagepassinganddensitysensitivesimilarity AT shifeiding animprovementofspectralclusteringviamessagepassinganddensitysensitivesimilarity AT hongjiejia animprovementofspectralclusteringviamessagepassinganddensitysensitivesimilarity AT lijuanwang improvementofspectralclusteringviamessagepassinganddensitysensitivesimilarity AT shifeiding improvementofspectralclusteringviamessagepassinganddensitysensitivesimilarity AT hongjiejia improvementofspectralclusteringviamessagepassinganddensitysensitivesimilarity |