Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex Rodríguez and Alessandro Laio attracted much attention in the field of pattern recognition and artificial intelligence. However, DPC still has a lot of defects that are not resolved. Firstly, the local density [Formula: see text] of point [Formula: see text] is affected by the cutoff distance [Formula: see text], which can influence the clustering result, especially for small real-world cases. Secondly, the number of clusters is still found intuitively by using the decision diagram to select the cluster centers. In order to overcome these defects, this paper proposes an automatic density peaks clustering approach using DNA genetic algorithm optimized data field and Gaussian process (referred to as ADPC-DNAGA). ADPC-DNAGA can extract the optimal value of threshold with the potential entropy of data field and automatically determine the cluster centers by Gaussian method. For any data set to be clustered, the threshold can be calculated from the data set objectively rather than the empirical estimation. The proposed clustering algorithm is benchmarked on publicly available synthetic and real-world datasets which are commonly used for testing the performance of clustering algorithms. The clustering results are compared not only with that of DPC but also with that of several well-known clustering algorithms such as Affinity Propagation, DBSCAN and Spectral Cluster. The experimental results demonstrate that our proposed clustering algorithm can find the optimal cutoff distance [Formula: see text], to automatically identify clusters, regardless of their shape and dimension of the embedded space, and can often outperform the comparisons.
Spectral clustering has become very popular in recent years, due to the simplicity of its implementation as well as the performance of the method, in comparison with other popular ones. But many studies show that clustering results are sensitive to the selection of the similarity graph and its parameters, e.g. [Formula: see text] and [Formula: see text]. To address this issue, inspired by density sensitive similarity measure, we propose an improved spectral graph clustering method that utilizes the similarity measure based on data density combined with DNA genetic algorithms (ISC-DNA-GA), making it increase the distance of the pairs of data in the high density areas, which are located in different spaces. The method can reduce the similarity degree among the pairs of data in the same density region to find the spatial distribution characteristics of the complex data. After computing the Laplacian matrix, we apply DNA-GAs to obtain the clustering centroids and assign all of the points to the centroids, so as to achieve better clustering results. Experiments have been conducted on the artificial and real-world datasets with various multi-dimensions, using evaluation methods based on external clustering criteria. The results show that the proposed method improves the spectral clustering quality, and it is superior to those competing approaches.
Three-dimensional geological and mining modelling in mining area is important for mine enterprises more productive, efficient, safe mining. Based on the available data such as cross-sections, drill-holes and geological maps, we developed to process geological information for building 3D model in this paper. Thus, special attention has been given to data structures and processing flows. Firstly, data arrange and sort with Geodatabase in ArcGIS; secondly, the 2D theme information is analyzed and stratigraphic is classified using geostatistical analysis method; thirdly, with the above preprocessing, the 3D model of Zhangji mine is built with 3DGeoModeller software. The results show that the overall 3D geometry of stratums below Zhangji mine (mainly coal seams) and the relationship of formations are then presented; the coal seams modelling and the theme information which suggests the presence of a relatively spatial distribution are analyzed; modelled geology on section is compared with the corresponding measured section. The 3D model built with multisource data is thus compatible with the geological data. The method is suit for 3D geological and mining modelling.
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