Simulation of Hybrid Attribute Data Clustering Optimization Based on BayesShrink Threshold Estimation
Unlike single attribute data,mixed attribute data usually has the characteristics of inconsistent scales.In order to obtain a more accurate mixed attribute clustering result,this paper put forward a mixed attribute clustering algorithm based on k-nearest neighbor.Firstly,the noise variance of mixed attribute data containing noise was accu-rately estimated using a high-frequency coefficient sliding window.Then,the optimal threshold was obtained through the Bayeshrink threshold estimation algorithm.Meanwhile,the mixed attribute data was denoised.Moreover,the k-nearest neighbor method was applied in data clustering,and the feature weight was added to the contribution of the de-noised data samples.Furthermore,the Euclidean distance of the feature weight after incorporating the contribution was calculated.The closer the distance,the larger the probability that the data belonged to the same category.After all the sample features were weighted,a mixed attribute clustering model was constructed.Finally,the particle swarm optimi-zation algorithm was used to optimize the model,thus obtaining the optimal weighted feature vector and realizing the clustering of mixed attribute data.Simulation results show that the proposed algorithm could effectively improve the accuracy and clustering efficiency of mixed attribute clustering results.
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