Compared with the conventional clustering algorithms, the proposed KRKM algorithm will measure both the reliabil- ity and the similarity for classifying data into its neighbor clusters by the...

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2014年9月1日 · In this paper, we introduced a novel Kernel-Reliability-based K-Means (KRKM) clustering algorithm for categorizing an unknown dataset under noisy condition.

In this paper, we put forward a Kernel Rough K-Means algorithm (KRKM) for RKM to solve nonlinear problem for RKM. KRKM expanded the ability of processing complex data of RKM and solve the problem of the soft clustering uncertainty.

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2014年11月1日 · The reliability distribution of our KRKM and RKM [6] algorithms around one fixed cluster center. It shows that KRKM is more efficient in rejecting outliers close to real clusters.

In this paper, we introduced a novel Kernel-Reliabilitybased K-Means (KRKM) clustering algorithm for categorizing an unknown dataset under noisy condition. Compared with the conventional clustering algorithms, the proposed KRKM algorithm will measure

Compared with the conventional clustering algorithms, the proposed KRKM algorithm will measure both the reliability and the similarity for classifying data into its neighbor clusters by the dynamic kernel functions, where the noisy data will be rejected by being given low reliability.

In this paper, we put forward a Kernel Rough K-Means algorithm (KRKM) for RKM to solve nonlinear problem for RKM. KRKM expanded the ability of processing complex data of RKM and solve the problem of the soft clustering uncertainty.

野外环境下移动机器人实时建模与自主行为优化方法研究，Kernel-Reliability-Based K-Means (KRKM) Clustering Algorithm and Image Processing，Chunsheng Hua, Juntong Qi, Jianda Han, Haiyu,，IEICE Transactions on Information and Systems