4results about How to "Accurate clustering" patented technology

The invention provides an abnormal capital collection account group identification, clustering and label labeling method based on capital flow data, and belongs to the technical field of financial risk prevention and control and data mining. A time sequence LSTM-structured self-encoding fusion AI feature extraction module is customized; the method comprises the following steps: capturing time sequence features such as periodic transfer and large-amount concentrated transfer through an LSTM attention layer, extracting structured features such as cross-regional association through an auto-encoder with a risk penalty term, and performing weighted fusion to obtain 12-dimensional AI features; and then semi-supervised K-means is used to identify a suspicious account group, spectral clustering is used to divide a case cluster, a random forest is used to label a'capitator / investor 'label, and the precision is ensured through three-layer verification. The method solves the problems of incomplete artificial feature coverage and poor universal AI adaptability in the prior art, and is suitable for abnormal capital investigation of financial supervision departments.

The application discloses a kind of based on OSM data's expressway interflow node identification and network construction method, first acquisition and pretreatment OSM expressway network and service area data, extract the main line of specified attribute, ramp element and uniform projection coordinate system;Again, by calculating the physical intersection of main line and ramp, the best clustering radius is determined in combination with K distance diagram, interflow area is obtained by DBSCAN clustering and the first type of cutting point is generated, while merging uplink and downlink service area and projecting to generate the second type of cutting point;Based on two kinds of cutting points, the main line section is divided, the end point of the section is matched with the cutting point with an error value of 1.0 meter, interflow, service area and temporary node are generated, and an initial topology graph is constructed;Finally, delete the temporary node connected to only two road sections and simplify the network by merging edges, output structured node and edge data.The application solves the problems of inaccurate interchange identification, road network redundancy and missing service area topology in traditional methods, and realizes the automatic and high-precision construction of expressway logical road network, which is suitable for macroscopic traffic analysis and path planning.

The application discloses a clustering division method and system suitable for magnetic resonance images and a magnetic resonance image information processing device. The method is realized by fusing magnetic resonance image features by using a multi-view algorithm. The method comprises the following steps: acquiring at least two feature similarity matrices according to magnetic resonance image information; fusing all the acquired feature similarity matrices by using a multi-view algorithm to obtain a fusion matrix, wherein the multi-view algorithm is realized by using a similarity network fusion method; and determining a clustering result of the magnetic resonance image information according to the obtained fusion matrix. The clustering division of the magnetic resonance image information is realized by extracting and fusing the magnetic resonance image features, and the clustering result is more accurate and more comprehensive.

This application discloses a method for determining developmental trajectories based on single-cell multi-omics clustering, belonging to the field of biomedical data mining technology. The method includes: acquiring multi-omics data of single cells from the same tissue; determining the potential representation of each single cell in different omics based on feature encoding technology; constructing a K-nearest neighbor graph for each omics based on the distance between single cells; and determining the corresponding multi-order similarity matrix; using the multi-order similarity matrix to complete the missing potential representations of single cells, obtaining the complete potential representation of each omics; performing cluster analysis on each omics to obtain single-cell clustering results; weighted fusion of the potential representations of each single cell in different omics to obtain a comprehensive potential representation; and analyzing the developmental trajectory of single cells based on the comprehensive potential representation. This application can stably and accurately cluster single cells under conditions of missing single-cell omics or significant differences in omics quality, thereby accurately determining the developmental trajectory of single cells.