35 results about "Statistical cluster" patented technology

Provided are, among other things, systems, methods and techniques for forecasting information. One representative embodiment involves obtaining data that has been provided by each of a number of individual people; dividing the people into clusters by utilizing a statistical clustering technique; calculating statistics of the data in each of at least some of the clusters; and combining the statistics for such clusters so as to obtain a combination forecast.

The invention provides a statistical clustering-based road intersection position identification method in trajectory data. The statistical clustering-based road intersection position identification method comprises the steps: performing trajectory data coordinate projection conversion and trajectory data simplification processing; for the simplified trajectory data, adopting a multi-core parallelcomputing mode, identifying sampling points in a road intersection area according to the steering change of the sampling points, and forming a steering sampling point set; taking the steering samplingpoint set as input, carrying out automatic division clustering by utilizing a self-adaptive statistical clustering algorithm, and stripping road intersections at different positions; and finally, foreach steering sampling point cluster, calculating a central position and a radius of a minimum circumscribed circle covering the cluster through a minimum circumscribed circle fitting algorithm, andrepresenting the central position and the regional range of the road intersection by using the center and the radius of the minimum circumscribed circle.

A technique for monitoring source addresses through statistical clustering of packets is disclosed. In one particular exemplary embodiment, the technique may be realized by a method for monitoring source addresses through statistical clustering of packets. The method may comprise identifying at least part of a source address of a packet. The method may also comprise searching at least one recorded source address based on the at least part of the source address, the at least one recorded source address being organized into at least one cluster. The method may further comprise routing the packet if the at least part of the source address falls within one of the at least one cluster and the one of the at least one cluster contains at least a predetermined number of source addresses.

A method of grouping retail units of a set of units in a chain uses, store and market-specific characteristics, including store profitability, to group stores into like economic markets. The relation between profits and prices defines markets; stores facing the same relation, that is the same profit function, are in the same economic market. These stores can follow similar pricing and promotion strategies. Multiple regression analysis is used to identify those characteristics that affect the relation between prices and profits (not simply variables correlated with profits). Upon suitable standardization and weighting, these variables are subsequently used with a statistical cluster analysis to classify units in two markets. Based on the estimated relationship and homegenity valuations from discriminant analysis, new stores can be more accurately added to the appropriate group.

A method of grouping retail units of a set of units in a chain uses store and market-specific characteristics, including store profitability, to group stores into like economic markets. The relation between profits and prices defines markets; stores facing the same relation, that is the same profit function, are in the same economic market. These stores can follow similar pricing and promotion strategies. Multiple regression analysis is used to identify those characteristics that affect the relation between prices and profits (not simply variables correlated with profits). Upon suitable standardization and weighting, these variables are subsequently used with a statistical cluster analysis to classify units in two markets. Based on the estimated relationship and homegenity valuations from discriminant analysis, new stores can be more accurately added to the appropriate group.

The invention relates to a POI name determination system based on clustering and a method thereof, wherein the method comprises the following steps: capturing address data from network data, the address data includes name field and address information; clustering name fields corresponding to the same address information according to key words; counting frequency of the clustered name fields occurred in each cluster, using the frequency as a second frequency; determining the POI name of the cluster corresponding to the address information based on the second frequency. By the provided technical scheme, users can quickly and precisely search the POI name corresponding to the POI address at the same longitude and latitude, thereby improving the user experience.

The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged intoa plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality ofhidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from eachstatistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

The invention relates to an image segmentation processing method based on an area matching optimization K-means clustering algorithm, comprising steps of firstly extracting vehicle feature points of front and back frame images, and then comparing an area overlapping situation of front and back frame vehicles, extracting positions of the feature points in an area overlapping region and the positions of the rest feature points, respectively calculating a mean value of the two groups of the feature points as two types of initial clustering central points to be segmented, and then implementing K-mean segmentation, correcting a classification situation of the feature points in the area overlapping region according to an output clustering result, and meanwhile, judging whether the clustered vehicles are reasonable or not; and if not, re-clustering the clustering result and recounting clustering centres, ending clustering segmentation until the reasonable vehicles are found, and then feeding back a tracking result. The method is on the basis of area matching optimization, and adopts fixed clustering numbers to implement the segmentation; and vehicle targets obtained by the K-mean segmentation do not need the next round of matching treatment, thereby a processing speed is accelerated, and time is saved.

The invention discloses a virtual machine load balancing method, a management module and a virtual machine cluster system, each manufacturer's virtual machine server is managed by the manufacturer's management module, and a management module responsible for cluster domain load balancing management is the main management module , the rest of the management modules are slave management modules, the master management module is responsible for statistics of virtual machine resource information and virtual machine load The collection and reporting of virtual machine resource information and virtual machine load information of the server, as well as the distribution of shared tasks, through the cooperation between the master and slave management modules, the virtual machine servers of different manufacturers can cooperate to share the load in the cluster domain, improving It improves the resource utilization and resource scheduling capabilities of the virtual machine server cluster in the data center.

The invention provides a method for identifying the location of road intersections in trajectory data based on statistical clustering, including trajectory data coordinate projection conversion, and trajectory data simplification processing; for the simplified trajectory data, a multi-core parallel computing method is adopted, and the direction is turned according to the sampling point The sampling points in the change recognition road intersection area constitute a set of steering sampling points; the set of steering sampling points is used as input, and the adaptive statistical clustering algorithm is used to automatically divide the clusters, and the road intersections at different locations are separated; finally, For each turning sampling point cluster, the center position and radius of the minimum circumscribed circle covering the cluster are calculated by the minimum circumscribed circle fitting algorithm, and the center position and area range of the road intersection are represented by the center and radius of the minimum circumscribed circle .

The invention discloses a method for retrieving a three-dimensional object, comprising the following steps: carrying out bipartite graph maximum matching on the three-dimensional retrieval object view sets and the view sets in a database, filtering the view sets not satisfying the preset matching conditions in the database to obtain the remaining view sets; carrying out bipartite graph optimal matching on the three-dimensional retrieval object view sets and each remaining view sets to acquire the distances between the three-dimensional retrieval object view sets and each remained view sets; sequencing each remaining view sets and outputting each sequenced remaining view sets as the retrieval results. The embodiment of the invention adopts pretreatment of statistical clustering, combines bipartite graph maximum matching and bipartite graph optimal matching to acquire the distances between the three-dimensional retrieval object view sets and each remaining view sets, thus improving the correctness of three-dimensional object retrieval, and simultaneously causing the three-dimensional object retrieval to be capable of not depending on collecting environment information, and the three-dimensional object retrieval method to be more vastly applied.

The invention provides a method and device for judging the occupation distribution of clustered users. The method comprises the following steps: obtaining a plurality of pieces of position information of a user terminal on the basis of positioning information provided by the user terminal; distinguishing the working place information and the residence plate information from the plurality of pieces of position information; clustering a plurality of users which are on the basis of the same residence place information; carrying out statistics on the working place information of the clustered users; and judging the occupation distribution of the clustered users according to the working place information of the clustered users. According to the method and device provided by the invention, the position information of the users is obtained by utilizing the positioning information of the users and then working place information and the residence place information are further distinguished; the plurality of users on the basis of the same residence place information are clustered to obtain the working place information of the clustered users in a certain region; and the occupations of the users can be determined on the basis of the working place information so as to obtain the occupation information of the work in the region and then the occupation distribution is analyzed.