33results about How to "Effective clustering" patented technology

A methods and apparatus for distributing users to clusters are provided. 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 data-field-based automatic clustering method. In the method, each space data is taken as a data point with mass, and the space data influence each other to form a data field, wherein total influence of all data points at a specific position represents the potential value of the data field. Points where the first-order partial derivative value of the potential value of the data field is zero are in an area where data superposition is most intensive, namely data cluster centers. The data-field-based automatic clustering method finds out the data cluster centers by searching the first-order derivative of the potential value of the data field, and searches the two sides according to the cluster centers and determines the edges of the clusters, thus finally marking complete clusters. Compared with the transitional clustering method, the data-field-based automatic clustering method has fast processing speed, is not influenced by noise, and is effective for clusters in any shapes, can be applied to the fields of image processing, community discovering, abnormal detection, market research and the like, thus improving the accuracy of the processing result.

The invention relates to a topic detection method based on document content and mutual relation. The method comprises the steps of preprocessing an obtained document to obtain a co-occurrence matrix and a two-two relation matrix of document features, constructing a target function on this basis, conducting iterative computation on a document representative degree matrix, a document subordination degree matrix, a word representative degree matrix and a word subordination degree matrix, and outputting the word representative degree matrix, wherein each row corresponds to one topic, and by using the word with the largest value in each row as a topic word for describing the topic, the topic word used for describing the topic is obtained. According to the topic detection method based on the document content and mutual relation, while document clustering and word clustering are conducted, joint clustering is more effective than respective clustering, a more comprehensive model is obtained when the relation of the document content and the relation among the documents are simultaneously considered compared with mere consideration of one information thereof, and through the introduction of the subordination degree and the representative degree, the method is not only applicable to clustering problems but also applicable to topic modeling problems.

The blockchain-based remote Internet big data smart medical system includes a vital sign collection module, a medical big data acquisition module, a blockchain storage module and a smart medical terminal. The health assessment model that evaluates the physical state of the physical sign data, inputs the patient's vital sign data collected by the vital sign collection module into the health assessment model, thereby assessing whether the patient's physical state is healthy or dangerous, and giving an early warning when it is dangerous. The beneficial effect of the present invention is that the remote monitoring of the patient's physical state is realized, and when the patient's physical state is in danger, an early warning can be given in time.

The invention discloses a data clustering-based robust scale invariant feature transform (SIFT) feature matching method, which comprises the following steps of: (1) acquiring reference image sequences, extracting SIFT feature sets, clustering all the SIFT feature sets by adopting a synthetic k-d data structure, and merging the repeated feature sets as clustering features, wherein the feature descriptors of the clustering features are expressed by adopting repeated feature average descriptors; (2) acquiring real-time image sequence feature sets by using an SIFT method, performing robust matching on the reference image sequences and the real-time image sequences, and selecting the corresponding reference image with maximum feature points as a key image to complete feature matching of a first stage; and (3) completing feature matching of a second stage by using the key image, removing exterior points by adopting the technologies of random sampling consistency (RANSAC), basic matrix and the like, and finally merging the feature matching results of the two stages. According to the matching method, the noise information interference can be reduced, and the robustness of feature matchingis greatly improved.

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.

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 point cloud misregistration filtering method and system for three-dimensional measurement of a complex special-shaped curved surface robot, and the method comprises the following steps: 1, dividing input registration point cloud pairs into three groups, selecting one point cloud pair from each group of registration point cloud pairs, and solving a corresponding transformation matrix Ri; 2, converting the obtained transformation matrix into an Euler rotation angle, and constructing an Euler rotation angle set D; Step 3, performing self-adaptive density clustering on the Euler rotation angles in the step D, selecting a class with the clustering result containing the maximum number of the Euler rotation angles, and taking a registration point cloud pair for solving the Euler rotation angles as a credible registration point cloud pair so as to complete error registration point cloud pair filtering; according to the method, spatial information of a given point cloud registration pair is fully utilized, effective decomposition and efficient clustering are carried out, so that false registration points in three-dimensional point cloud registration can be effectively eliminated, and the method has relatively high adaptability to noise of point cloud, registration position deviation and the like. The system is simple in structure and convenient to operate.