Skip to Main content Skip to Navigation
Conference papers

A K-AP Clustering Algorithm Based on Manifold Similarity Measure

Abstract : K-AP clustering algorithm is a kind of affinity propagation (AP) clustering that can directly generate specified K clusters without adjusting the preference parameter. Similar to AP clustering algorithm, the clustering process of K-AP algorithm is also based on the similarity matrix. How to measure the similarities of data points is very important for K-AP algorithm. Since the original Euclidean distance is not suit for complex manifold data structure, we design a manifold similarity measurement and proposed a K-AP clustering algorithm based on the manifold similarity measure (MKAP). If two points lie on the same manifold, we assume that there is a path inside the manifold to connect the two points. The manifold similarity measure uses the length of the path as the manifold distance between the two points, so as to compress the distance of the data points in high-density region, while enlarge the distance of data points in low-density region. The clustering performance of the proposed MKAP algorithm is tested by comprehensive experiments. The clustering results show that MKAP algorithm can well deal with the datasets with complex manifold structures.
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-02197788
Contributor : Hal Ifip <>
Submitted on : Tuesday, July 30, 2019 - 5:01:37 PM
Last modification on : Tuesday, July 30, 2019 - 5:12:20 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Hongjie Jia, Liangjun Wang, Heping Song, Qirong Mao, Shifei Ding. A K-AP Clustering Algorithm Based on Manifold Similarity Measure. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.20-29, ⟨10.1007/978-3-030-00828-4_3⟩. ⟨hal-02197788⟩

Share

Metrics

Record views

48