| Autor: |
K. Mrudula, T. Hitendra Sarma |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 1st International Conference on Electrical, Control and Instrumentation Engineering (ICECIE). |
| DOI: |
10.1109/icecie47765.2019.8974790 |
| Popis: |
Kernel versions of FCM have been proved to be better than FCM in identifying overlapping and linearly inseparable clusters in the input space. Kernel FCM-F (KFCM-F) and Kernel FCM-K (KFCM-K) are the two kernel versions of FCM. In KFCM-F the cluster centers are considered in the feature space, where as in KFCM-K the cluster centers are identified in the kernel space. KFCM-K is superior than KFCM-F w.r.t the clustering quality, but it is not applicable on large data sets because of its quadratic time complexity i.e., $O(n^{2})$ where $n$ is the size of the data set. This paper propose a new prototype based hybrid technique to speed-up KFCM-K for large data sets. The proposed method initially identifies some representative data items from the given data, say $l$ where $l , in linear time. The conventional kernel FCM-K is then applied over these representatives. As $l the clustering time is reduced to $O(n+l^{2})$ from $O(n^{2})$ . Experimental study on several benchmark data sets shows that the proposed method converges in less time when compare to conventional KFCM-K, but with a negligible deviation in the clustering quality. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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