Environment Parameter Rating Evaluation for Smart Museum Based on Improved K-Means Clustering Algorithm

The cultural heritage is a unique and irreplaceable witness of our past and it is vulnerable to natural disasters and anthropic behaviors. A rating evaluation system for smart museum environment parameter can serve as a reference for decision-making. However, this field is in short in preventive conservation strategies aimed to assure the protection, or increase the life expectancy in museums. This paper proposes a rating evaluation for museum environment parameter data based on improved K-Means clustering algorithm by introducing weighted cluster center function. Presented improved K-Means improves the clustering effect by weakening the random initial cluster center selection easy to fall into the local optimal results. Experimental results demonstrate that the improved clustering algorithm is not only more stable in clustering process, but can reduce the impact of the noise data. The presented rating evaluation for museum environment parameter data can provide the basis for reasonable, effective regulation and control of museum environmental parameters.