KEYWORDS: Model-based design, Digital signal processing, Seaborgium, Pattern recognition, Data modeling, Mining, Sun, Lutetium, Information science, Data mining
Due to the curse of dimensionality, traditional clustering methods usually fail to produce meaningful results for the high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this paper, we first construct a graph G from the data by defining an adjacency relationship between the data points using Shared Reverse k Nearest Neighbors (SRNN). Then a hypergraph is created from the graph G by defining the hyperedges to be all the maximal cliques in the graph G. After the hypergraph is produced, a powerful hypergraph partitioning method called dense subgraph partition (DSP) combined with the k-medoids method is used to produce the final clustering results. The proposed method is evaluated on several real high-dimensional datasets, and the experimental results show that the proposed method can improve the clustering results of the high dimensional data compared with applying k-medoids method directly on the original data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.