32results about How to "Effective clustering" patented technology

The invention discloses a clustering method based on mobile object spatiotemporal information trajectory subsections. The clustering method based on mobile object spatiotemporal information trajectory subsections comprises the steps that the three attributes of time, speed and direction are introduced, and a similarity calculation formula of the time, speed and direction is provided for analyzing an internal structure and an external structure of a mobile object trajectory; firstly, according to the space density of the trajectory, the trajectory is divided into a plurality of trajectory subsections, then the similarities of the trajectory subsections are judged by calculating differences of the trajectory subjections on the space, time, speed and direction, finally, trajectory subsections in a non-significant cluster are deleted or are merged into adjacent significant clusters on the basis of a first cluster result, and therefore an overall moving rule is displayed on the clustering spatial form. According to the clustering method based on the mobile object spatiotemporal information trajectory subsections, the clustering result is improved, higher application value is provided, a space quadtree is adopted to conduct indexing on the trajectory subsections, clustering efficiency is greatly improved under the environment of a large-scale trajectory number set, and trajectories can be effectively clustered.

The invention relates to a vector-quantization-based overall and local color image searching method. The invention provides a new color image searching method, and relates to the field of an image processing technology. The method comprises the steps of converting an RGB (red, green and Blue) color space into an HSV (Hue, Saturation, Value) color space, performing relatively accurate clustering division on the color space by applying a neural-network-based competitive learning algorithm training code book; describing a space distribution situation of colors by introducing a color transfer matrix; combining the two characteristics, namely an index column diagram and a main color transfer matrix, so as to perform the similarity measurement; processing images by applying a morphological opening-and-closing operation, highlighting a target contour so as to extract a local interest area so as to highlight an important area and limit background information. The color image searching method overcomes the defects that the color space distribution description is not enough and the background information cannot be effectively limited by using the overall color histogram method. By using the vector-quantization-based overall and local color image searching method, the color quantization is relatively accurate, the matching effect is relatively good, and the vector-quantization-based overall and local color image searching method is an effective method for further improving the researching efficiency.

Methods and apparatus, including systems and computer program products, to provide clustering of users in which users are each represented as a set of elements representing items, e.g., items selected by users using a system. In one aspect, a program operates to obtain a respective interest set for each of multiple users, each interest set representing items in which the respective user expressed interest; for each of the users, to determine k hash values of the respective interest set, wherein the i-th hash value is a minimum value under a corresponding i-th hash function; and to assign each of the multiple users to each of the respective k clusters established for the respective user, the i-th cluster being represented by the i-th hash value. The assignment of each of the users to k clusters is done without regard to the assignment of any of the other users to k clusters.

The invention relates to a personalized recommendation method based on dynamic neighboring point spectral clustering. A user-store bipartite network is set up according to user sign-in information; the user-store bipartite network is projected to a user-user one-aside network and a store-store one-aside network, and a node2vec algorithm is used for projecting the two weighted one-aside networks totwo different vector spaces; the spectral clustering algorithm based on dynamic neighboring points is called to cluster user vectors and store vectors obtained above to obtain multiple user clustersand multiple store clusters; the sign-in information existing between single users is converted into a cluster network among the user clusters and the store clusters; a K-means algorithm is used for dividing the one-dimension vector into two classes, the store clusters in the class with the larger sign-in average number are recommended to the user clusters; personalized recommendation is conductedaccording to each user cluster and the recommended store clusters. The accuracy of the recommendation method is improved effectively.

The invention discloses a stratospheric active routing design method based on a delay/disruption tolerant network. The active routing process is conducted in a manner of clustering, layering and cruising by unmanned aerial vehicles. The method comprises the following steps: firstly, dividing a user node into a plurality of mutually disjointed regions according to geographic positions through a clustering algorithm, and selecting an aggregation node of each region; secondly, sending the message to the aggregation node through the unmanned aerial vehicle in the region when a node in the region hopes to send a message to a user outside the region; thirdly, cruising the aggregation nodes through the unmanned aerial vehicle in the whole network; finally, uploading a message that a destination does not belong to the region to the unmanned aerial vehicle through the aggregation nodes when the unmanned aerial vehicle in the whole network passes through the aggregation nodes, forwarding the message to the aggregation node of the destination region thorugh the unmanned aerial vehicle in the whole network, and forwarding the message to the destination node through the unmanned aerial vehicle of the destination aggregation node.

The invention discloses a data clustering method for modal incomplete alignment. The method comprises: S1, acquiring a plurality of modal data sets, taking one modal data set as alignment modal data,and remaining analog non-alignment modal data; S2, respectively inputting each modal data set into one self-encoding network; S3, calculating a distance matrix of an alignment mode and a non-alignmentmode; S4, sending the distance matrix of the non-alignment modal data into a differentiable alignment module to calculate a prediction permutation matrix; S5, calculating a loss value by adopting a loss function; S6, performing back propagation based on the loss value to optimize the self-encoding network; S7, respectively inputting the modal data sets in the step S1 into the optimized self-encoding networks corresponding to the modal data sets; S8, obtaining a new prediction permutation matrix by adopting the execution modes of S3 and S4, and permuting the public representation output in thestep S7 by adopting the new prediction permutation matrix to obtain an aligned public representation; and S9, splicing the common representations output in the step S8, and then performing clusteringto obtain a clustering result.

The invention relates to a medical image clustering method, and belongs to the field of image processing. The method is based on a downhill algorithm, and comprises the following steps of preprocessing an image, extracting an image characteristic, searching a density attractor, searching hill foot pixels, and separating a layout image. Theories and practices prove that the method achieves a ridge, a flat top and a single threshold appropriately, and after the method is adopted, the medical image can be clustered efficiently, and the image characteristic is not lost, misled or omitted, so that the high-quality medical clustering image can be obtained, and diagnosis and reading requirements of medical workers are met.

The invention discloses a user load classification method based on a basic load reduction strategy, and the method comprises the steps: 1, extracting user load data, and carrying out the preprocessingof the load data; Step 2, selecting a load reduction reference value, taking the aggregation degree as an evaluation parameter, and distinguishing load types by adopting a method of reducing a reference load value; and step 3, clustering the load data by adopting a fuzzy C-means algorithm according to a load type distinguishing result. Aiming at the problem that the user type is mistakenly identified due to the user scale in the current load clustering method, the invention provides an improved load clustering algorithm processing skill, and the accuracy of the algorithm provided by the invention is verified by utilizing a fuzzy algorithm. The method adopted by the invention can effectively eliminate the interference of the user scale on the user type.

The invention discloses a membrane calculation data cell clustering method oriented to the field of big data. through a k-means algorithm based on density, data is preprocessed, and the defect of manually reserving the number of the data clusters is overcome, and relatively reasonable data cluster division is obtained. A hybrid evolution mechanism adopts an Agnes algorithm based on hierarchical division, a genetic algorithm (GA) and a weighted fuzzy clustering (FCM) algorithm as evolution rules, and the advantages of the three clustering algorithms can be effectively combined in combination with membrane calculation, so that a better clustering result can be obtained.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent prediction method for fusing fundus color pictures and deep learning myocardial infarction. Themethod comprises the specific steps of labelling and preprocessing collected fundus color pictures on the basis of longitudinal follow-up visiting data of a database; constructing a deep learning network, and optimizing deep parameters; obtaining patient fundus color pictures to be recognized, utilizing a deep learning intelligent model for recognizing fundus characteristics, and obtaining an intelligent classification result for judging whether or not myocardial infarction exists in the fundus color pictures; the method can efficiently, conveniently and non-invasively perform myocardial infarction high-risk group screening.

The invention belongs to the technical field of wireless communication, and discloses a user clustering method and system, a storage medium, a computer device and an application. The method comprisesthe steps: carrying out the initial user clustering according to the channel gains of all users in the system and the channel similarity between the users; calculating initial model parameters of theGaussian mixture model according to an initial user clustering result; calculating the posterior probability of each sample generated by each mixed component and iteratively updating the parameters ofthe Gaussian mixture model; and judging whether the number of iterations reaches a set termination condition or not: if so, performing user clustering according to a cluster mark, and otherwise, returning to calculate the posteriori probability of each sample generated by each mixed component and iteratively updating the parameters of the Gaussian mixture model. According to the method, the initial model parameters of the Gaussian mixture model are optimized, the system throughput is improved compared with a conventional user clustering method, and the method can be used for a multi-antenna non-orthogonal multiple access system.