KEYWORDS: Data modeling, Education and training, Data conversion, Feature extraction, Neural networks, Matrices, Reconstruction algorithms, Data storage, Model-based design, Reflection
Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity; then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder; then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets.
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.