Locality-sensitive hashing K-means algorithm for large-scale datasets

Efficient processing strategy for large datasets is a key support for coal mine intelligent constructions, such as the intelligent construction of coal mine safety monitoring and mining. To address the problem of insufficient clustering efficiency and accuracy of the K-means algorithm for large datasets, a highly efficient K-means clustering algorithm based on locality-sensitive hashing (LSH) is proposed. Based on LSH, the sampling process is optimized, and a data grouping algorithm LSH-G is proposed. The large dataset is divided into subgroups and the noisy points in the dataset are removed effectively. Based on LSH-G, the subgroup division process in the density biased sampling (DBS) algorithm is optimized. And a data group sampling algorithm, LSH-GD, is proposed. The sample set can more accurately reflect the distribution law of the original dataset. On this basis, the K-means algorithm is used to cluster the generated sample set, achieving efficient mining of effective data from large datasets with low time complexity. The experimental results show that the optimal cascade combination consists of 10 AND operations and 8 OR operations, resulting in the smallest sum of squares due to error of class center (SSEC). On the artificial dataset, compared with the K-means algorithm based on multi-layer simple random sampling (M-SRS), the K-means algorithm based on DBS, and the K-means algorithm based on grid density biased sampling (G-DBS), the K-means algorithm based on LSH-GD achieves an average improvement of 56.63%, 54.59%, and 25.34% respectively in clustering accuracy. The proposed algorithm achieves an average improvement of 27.26%, 16.81%, and 7.07% in clustering efficiency respectively. On the UCI standard dataset, the K-means clustering algorithm based on LSH-GD obtains optimal SSEC and CPU time consumption (CPU-C).