A New Cross Clustering Algorithm for Improving Performance of Supervised Learning
| Autor: | Xin Cai, Chunli Jiang, Lei Chen, Kuangrong Hao, Wenshuo Zhou, Xue-song Tang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Series (mathematics) time series classification Computer science business.industry Cross clustering nearest neighbor algorithm Supervised learning General Engineering Data transformation (statistics) Pattern recognition k-nearest neighbors algorithm full convolutional network Dimension (vector space) General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering Performance improvement Cluster analysis business Time complexity lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 56713-56723 (2019) |
| ISSN: | 2169-3536 |
| Popis: | In this paper, a new clustering algorithm is proposed based on cross clusters without using membership functions. In light of the cross clustering data transformation, the spatial distribution of data is changed while the original data dimension simultaneously is maintained. Combining with the performance index and visual technology, an explanation of the performance improvement of the classification model is presented in accordance with the proposed algorithm. This approach was evaluated on UCR time series datasets, the experiments showed that the algorithm can improve not only the accuracy and the performance of the fully convolutional network and nearest neighbor algorithm, but also the time complexity in time series classification model. It is worth well to apply this method to further research and popularization. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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