A K-means Text Clustering Algorithm Based on Subject Feature Vector

As one of the most popular clustering algorithms, k-means is easily influenced by initial points and the number of clusters, besides, the iterative class center calculated by the mean of all points in a cluster is one of the reasons influencing clustering performance. Representational initial points are selected in this paper according to the decision graph composed by local density and distance of each point. Then we propose an improved k-means text clustering algorithm, the iterative class center of the improved algorithm is composed by subject feature vector which can avoid the influence caused by noises. Experiments show that the initial points are selected successfully and the clustering results improve 3%, 5%, 2% and 7% respectively than traditional k-means clustering algorithm on four experimental corpuses of Fudan and Sougou.

Suspect Multifocus Image Fusion Based on Sparse Denoising Autoencoder Neural Network for Police Multimodal Big Data Analysis

In recent years, the success rate of solving major criminal cases through big data has been greatly improved. The analysis of multimodal big data plays a key role in the detection of suspects. However, the traditional multiexposure image fusion methods have low efficiency and are largely time-consuming due to the artifact effect in the image edge and other sensitive factors. Therefore, this paper focuses on the suspect multiexposure image fusion. The self-coding neural network based on deep learning has become a hotspot in the research of data dimension reduction, which can effectively eliminate the irrelevant and redundant learning data. In the case of limited field depth, due to the limited focusing depth of the camera, the focusing plane cannot obtain the global clear image of the target in the depth scene, which is prone to defocusing and blurring phenomena. Therefore, this paper proposes a multifocus image fusion based on a sparse denoising autoencoder neural network. To realize an unsupervised end-to-end fusion network, the sparse denoising autoencoder neural network is adopted to extract features and learn fusion rules and reconstruction rules simultaneously. The initial decision graph of the multifocus image is taken as a prior input to learn the rich detailed information of the image. The local strategy is added to the loss function to ensure that the image is restored accurately. The results show that this method is superior to the state-of-the-art fusion methods.

Computing salient feature points of 3D model based on geodesic distance and decision graph clustering

Introduction:: Computing salient feature points (SFP) of 3D models has important application value in the field of computer graphics. In order to extract more effectively, a novel SFP computing algorithm based on geodesic distance and decision graph clustering is proposed. Method:: Firstly, geodesic distance of model vertices is calculated based on heat conduction equation, then average geodesic distance and importance weight of vertices are calculated. Finally, decision graph clustering method is used to calculate the decision graph of model vertices. Result and Discussion:: 3D models in SHREC 2011 dataset are selected to test the proposed algorithm. Compared with the existing algorithms, this method calculates the SFP of the 3D model from a global perspective. Results show that it is not affected by model posture and noise. Conclusion:: Our method maps the SFP of 3D model to 2D decision-making diagram, which simplifies the calculation process of SFP, improves the calculation accuracy and has strong robustness.

An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.