865 results about "Hierarchical clustering" patented technology

A virtual routing platform includes a line interface a plurality of virtual routing engines (VREs) to identify packets of different packet flows and perform a hierarchy of metering including at least first and second levels of metering on the packet flows. A first level of metering may be performed on packets of a first packet flow using a first metering control block (MCB). The first level of metering may be one level of metering in a hierarchy of metering levels. A second level of metering on the packets of the first packet flow and packets of a second flow using a second MCB. The second level of metering may be another level of metering in the hierarchy. A cache-lock may be placed on the appropriate MCB prior to performing the level of metering. The first and second MCBs may be data structures stored in a shared memory of the virtual routing platform. The cache-lock may be released after performing the level of metering using the MCB. The cache-lock may comprise setting a lock-bit of a cache line index in a cache tag store, which may identify a MCB in the cache memory. The virtual routing platform may be a multiprocessor system utilizing a shared memory having a first and second processors to perform levels of metering in parallel. In one embodiment, a virtual routing engine may be shared by a plurality of virtual router contexts running in a memory system of a CPU of the virtual routing engine. In this embodiment, the first packet flow may be associated with one virtual router context and the second packet flow is associated with a second virtual router context. The first and second routing contexts may be of a plurality of virtual router contexts resident in the virtual routing engine.

The invention relates to a news event testing method based on metadata analysis, belonging to the technical field of data mining. The invention is characterized in that: a multi-dimensional vector model is used to represent news documents, and the time characteristics of news are given adequate consideration during the weight calculation of the characteristic representation, and an improved IDF (inverse document frequency) calculation method for news characteristic words. During calculating the similarity among different pieces of news, such information as the time, categories and specific contents of the news are taken into comprehensive consideration, the news documents are preprocessed by extracting the keywords, and thus the dimensionality of the vector is greatly reduced. Subsequently, the news reports are clustered by means of the hierarchical clustering method, and the clustering result tree is then partitioned to cluster the new reports and accordingly matched with relevant news events. Compared with the prior event testing method, the invention has the advantage of a higher F value, which is used as a standard to assess the quality of clustering.

The invention provides for quickly browsing through a large set of video clips to locate video clips of interest. In an embodiment of the present invention, hierarchical clustering of the video clips can be undertaken enabling the user to successively identify the subgroup of video clips of interest. This approach generates a video summary for the contents of each cluster by selecting representative video clips from individual videos and lower level clusters within the cluster. Links are added between the more general, higher-level clusters and the elements they contain. Thus, starting at the top of the set of videos being browsed or returned by the search engine and continuing at each subsequent cluster level, the user is presented with video summaries for the relevant parts of videos and those of next lower-level clusters. The user can then follow the navigational link to the desired video or lower-level cluster.

Provided herein are methods for diagnosing and treating multiple myeloma based on statistical analysis of and subsequent increasing / inhibiting expression of subgroups of plasma cells and B cell genes. Also provided are methods for a developmental stage-based classification for multiple myeloma using hierarchical clustering analysis of plasma cell and B cell nucleic acids and for discriminating among normal, hyperplastic and malignant using gene expression array data and statistical analysis thereof. In addition methods for determining the risk of developing bone disease in a test individual by examining expression levels of a WNT signaling antagonist, such as DKK1, are provided. A kit comprising anti-DKK1 antibodies and detection reagents for measuring DKK1 protein levels also is provided.

The invention relates to a point cloud data based single tree three-dimensional modeling and morphological parameter extracting method. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method comprises obtaining three-dimensional surface point cloud data of high density standing trees through a three-dimensional scanner or other live-action measuring modes, calculating the shortest distance from points to root nodes through a k-nearest neighbor graph, performing hierarchical clustering on the data according to distance, enabling centers of clustering hierarchies to be served as framework points of a limb system and meanwhile extracting corresponding semi-diameter of the framework points; connecting the framework points to establish a topological structure of branches and grading the branches; performing three-dimensional geometrical reconstruction on branches through a generalized cylinder body; adding leaf models to the limb system to form into a vivid three-dimensional single tree model; extracting height of trees, diameter of breast height and crown breadth of the standing trees in the point cloud. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method can rapidly and semi-automatically extract tree important geometrical parameters and topological information to form into the high vivid single tree geometric model and has wide application prospects and values in fields such as agriculture and forestry survey, ecological research and landscape planning.

An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are extracted from the document collection. Documents are the indexed according to their included phrases, using phrase posting lists. The phrase posting lists are stored in an cluster of index servers. The phrase posting lists can be tiered into groups, and sharded into partitions. Phrases in a query are identified based on possible phrasifications. A query schedule based on the phrases is created from the phrases, and then optimized to reduce query processing and communication costs. The execution of the query schedule is managed to further reduce or eliminate query processing operations at various ones of the index servers.

A probabilistic clustering system is defined at least in part by probabilistic model parameters indicative of word counts, ratios, or frequencies characterizing classes of the clustering system. An association of one or more documents in the probabilistic clustering system is changed from one or more source classes to one or more destination classes. Probabilistic model parameters characterizing classes affected by the changed association are locally updated without updating probabilistic model parameters characterizing classes not affected by the changed association.

Systems, apparatuses, and methods are described for incrementally adding items received from an input stream to a cluster hierarchy. An item, such as a document, may be added to a cluster hierarchy by analyzing both the item and its relationship to the existing cluster hierarchy. In response to this analysis, a cluster hierarchy may be adjusted to provide an improved organization of its data, including the newly added item.

The present invention provides a Distinct Author Identification System (“DAIS”) for disambiguating data to discern author entities and link or associate authorships with such author entities. The invention provides powerful disambiguation processes applied across one or more databases to yield a disambiguated authority database of authors. An entire database of publications may be processed by the DAIS to group / link authorships and to identify author entities. The author entities may then be matched or associated with actual authors to establish an authority database of authors. After initial evaluation, the DAIS may be used to reevaluate some or all of the database(s) and / or the authority database established by the DAIS may be used to add or update information. DAIS may use “hierarchical clustering” to link authorships and identify authors based on authorship similarity. DAIS evaluates the likelihood that authorships are from the same author.

The invention discloses a multi-dimensional feature integrated building point cloud hierarchical clustering segmentation method. The method comprises the steps of carrying out initial segmentation on mutually discontinuous point cloud data in building point cloud data; through utilization of a G-K (Gustafson-Kessel) clustering algorithm and through combination of the spectral characteristics of the point cloud data, carrying out first layer fine segmentation on the point cloud data; and carrying out second layer fine segmentation on obtained normal vector characteristics and curvature characteristics of the point cloud data, wherein two times of segmentation are repeated until demands are satisfied. According to the building point cloud hierarchical clustering segmentation method, the density information of the point cloud data is scanned through utilization of multi-dimensional laser; on the premise of no prior knowledge, the initial segmentation is carried out on a plurality of point cloud data blocks which are spaced at relatively long distance and are relatively dense; moreover, through combination of the spectral characteristics and geometrical characteristics of the point cloud data, the fine segmentation is carried out on the initially segmented point cloud data blocks, until each point cloud data block has a single geometrical characteristic and modeling can be carried out through utilization of a simple mathematical model. According to the method, the building point cloud data can be extracted from surroundings, the building point cloud can be decomposed into different planes, and a good foundation is laid for reconstructing a building.

The invention discloses a method for reconstructing an outer outline polygon of a building based on multivariate data. The method comprises the following steps of: respectively dividing DSM (Design Standards Manual) data and image data so as to obtain a mask image of an interest region of the building and an image dividing object; combining the mask image with the image dividing object so as to obtain a complete building object; carrying out boundary tracing on the building object so as to obtain curves of the building; using points corresponding to local maximum curvature values of the curves as angular points; connecting the angular points in sequence so as to obtain the outline polygon of the building; dividing the building object into regions by using a hierarchical clustering method and calculating the main direction of the building; establishing a linear model of the polygon of the building and correcting and regularizing the linear model of the outline of the building with the combination of the main direction of the building and gradient information of the image data; calculating an intersection point of every two adjacent straight line sections by using the linear model of each line section of the polygon; and by taking the intersection points as the angular points, connecting the angular points in sequence so as to form the final polygon of the building. According to the method, the DSM data is organically combined with the image data, the data are complementary to each other in the whole process, so that the problem of reconstructing the polygon of the outline of the building is solved well, and the method has very strong robustness in the two-dimensional outline modeling aspect of the building.

The invention provides a microblog-oriented dynamic topic detecting and evolution tracking method and belongs to the technical field of intelligent information processing. The method includes the steps of 1, establishing a distributed crawler to acquire microblog data; 2, pre-processing the microblog data; 3, performing Chinese word segmentation to remove stop words, and acquiring a word set VOC; 4, subjecting the microblog data to LDA (latent Dirichlet allocation) clustering in each time interval so as to extract latent topics; 5, screening out microblog hot topics in each time interval; 6, subjecting the hot topics of a global time to hierarchical clustering to acquire inter-topic aggregation and differentiation relations; 7, visualizing a topic evolution process according to the inter-topic aggregation and differentiation relations. The method has the advantages such that topic word distribution of an event in different times and a fine-grained topic of a same topic in different times are mined under low time complexity, efficiency is high, and robustness is high; the method has greater practical value.

The invention relates to a WAMS (wide area measurement system) based low-frequency oscillation coordinated damping control method for a power system, and belongs to the technical field of low-frequency oscillation analysis and control of power systems. The method includes adopting a hierarchical clustering technology to perform primary region division according to power angle curves or angular velocity curves obtained after preprocessing, performing low-frequency oscillation mode identification on the system on the basis of a Prony identification algorithm, adopting a PSS (power system stabilizer) to inhibit regional oscillation modes for strong correlation generators with generated low-frequency oscillation modes belonging to regional oscillation modes, introducing wide-area signals of other regions for generators with generated low-frequency oscillation modes belonging to inter-region oscillation modes, designing a controller for a reduced mathematic model of an identification system, and solving parameters of the controller by an LMI (linear matrix inequality). By the low-frequency oscillation controller design method, in a regional and hierarchical control mode, coordinated damping control of the power system is realized.

Gene expression profiling and hierarchical clustering analysis readily identify differential gene expressions in normal renal epithelial cells and renal cell carcinomas. Genes identified by this analysis would be useful for diagnosis, prognosis and development of targeted therapy for the prevention and treatment of conventional renal cell carcinoma.

Storing data mining clustering results in a relational database for querying and reporting, including reading, from a hierarchical clustering node, clustering data describing a clustering, and recording the clustering data in a relational clustering record; reading, from a hierarchical cluster node embodied in the hierarchical representation of data mining results, cluster data describing a cluster, and recording the cluster data in a relational cluster record; reading, from a hierarchical cluster attribute node embodied in the hierarchical representation of data mining results, cluster attribute data describing a cluster attribute, and recording the cluster attribute data in a relational cluster attribute record; reading, from a hierarchical cluster attribute bin node embodied in the hierarchical representation of data mining results, cluster attribute bin data describing a cluster attribute bin, and recording the cluster attribute bin data in a relational cluster attribute bin record.

A system and method for grouping segments of data sequences into clusters is a hierarchical clustering method that groups data points into clusters that are globular or compact. Cluster sets can be constructed only for each select level of a hierarchical sequence. Whether a level of a hierarchical sequence is meaningful is determinable prior the beginning of when the corresponding cluster set is constructible.

The invention discloses a mango picking point recognition method which comprises the following steps: collecting mango images, and establishing a mango picking image library in a natural scene; establishing a mango fruit segmentation model based on a Mask R-CNN network ; calculating the long axis, the short axis and the mass center of each fruit; judging whether a cluster is formed or not by usinga bottom-up hierarchical clustering method; If the mango fruits are clustered, cluster fruit mother branches are identified, and picking points are positioned on the mother branches; and if the mangois a single fruit, segmenting and identifying fruit stems of the fruit, and determining picking points on the fruit stems. Mask-based R-is utilized in the invention. The mango fruit segmentation model of the CNN network carries out fruit instance segmentation, solves the detection segmentation problem caused by light change, shielding and overlapping in a natural orchard scene, and has the advantages of accurate segmentation and multiple application scenes.

The invention relates to a method for predicting city traffic accidents based on time-space distribution characteristics. The method comprises: first, in combination of the case information and the space information, creating a case space database and performing pretreatment to the data; then, based on surface area statistics, analyzing the traffic accidents' time-space distribution characteristics; using the global and local self-correlation method to realize the analyzing of the aggregate state; based on the case happening point data, analyzing the traffic accidents' time-space distribution characteristics; through the hierarchical clustering analysis, expressing the distribution rule of the cases hierarchically; through the nuclear density estimation method, expressing the continuous changes and accurate gathering center of the traffic accidents' happening distribution; and finally, utilizing the BP neural network prediction algorithm, using the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future. According to the invention, in combination with the time-space distribution and through the utilization of big date excavation BP neural network prediction algorithm and the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future, it is possible to increase the precision, the timeliness and reduce the manual cost.

The invention relates to a method and a system for discovering a user residence position based on mobile phone signaling. The method comprises the following steps: the obtained mobile phone signalingdata of the user is taken as the original trajectory data, and the original trajectory data is sequentially processed by data cleaning, data slicing, preliminary clustering, data verification and corecluster calculation, and finally the user staying position is obtained. Adopting a series of algorithms such as track slice aggregation, hierarchical clustering algorithm, kernel density clustering algorithm and machine learning algorithm, etc., considering mobile phone track point density, signaling time interval, track point moving direction and moving distance, etc., we can judge user 's staying position, staying time, entering and leaving staying point time. The algorithm can effectively eliminate the interference of base station drift on user location judgment, improve the identificationaccuracy of user dwell point position and dwell time, and objectively restore the user dwell position.

Techniques to detect fraud through behavioral analysis with low false positives are disclosed. In various embodiments, resource access data indicating for each resource in a set of resources respective usage data for each of one or more users of the resource is received. Hierarchical clustering analysis is performed to determine at each of two or more hierarchical levels a set of one or more clusters of users, resources, or both. A level-specific anomaly score is computed at each of said two or more hierarchical levels. The level-specific anomaly scores are aggregated across said two or more hierarchical levels to determine an aggregate anomaly score. The aggregate anomaly score to determine whether an anomaly has been detected.

Monoclonal gammopathy of undetermined significance can progress to multiple myeloma. Applying significance analysis of microarrays, 52 genes, involved in important pathways related to cancer, were differentially expressed between plasma cells from healthy subjects and patients with stringently defined monoclonal gammopathy of undetermined significance / smoldering multiple myeloma and symptomatic multiple myeloma. Unsupervised hierarchical clustering of 351 multiple myeloma and 44 cases of monoclonal gammopathy of undetermined significance and 16 cases of multiple myeloma with a monoclonal gammopathy of undetermined significance history, created two major cluster branches, one containing 82% of the monoclonal gammopathy of undetermined significance cases and 28% of the multiple myeloma, termed monoclonal gammopathy of undetermined significance-like multiple myeloma. Using the same clustering approach on an independent cohort of 213 cases of multiple myeloma revealed 27% with monoclonal gammopathy of undetermined significance-like multiple myeloma which, despite a lower incidence of complete remission, was associated with low-risk clinical and molecular features and superior survival. The monoclonal gammopathy of undetermined significance-like multiple myeloma signature was also seen in patients surviving more than 10 years after autotransplant.

The invention belongs to the field of data mining, natural language processing and information retrieval, and provides a news topic detection method. For the defect of a TF-IDF-based vector space algorithm in semantics and the defects of time complexity and accuracy of textual level clustering, feature extraction, representation modeling, similarity calculation and quick and accurate text clustering methods for a large amount of news texts are improved. The LDA fusion model and multilayer clustering-based news topic detection method comprises the following steps of 1: building a similarity model by using a vector space model (VSM); 2: finally obtaining accurate parameter settings; 3: organically fusing two text models; 4: judging whether a topic is a new topic or not; 5: calculating the similarity until all documents are clustered; and 6: adding an ISP&AH clustering algorithm of AHC based on the step 5. The method is mainly applied to the design and manufacturing occasions.

The method for generating an integrated guidance law for aerodynamic missiles uses a strength Pareto evolutionary algorithm (SPEA)-based approach for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The distribution of membership functions and the associated rules are obtained by solving a nonlinear constrained multi-objective optimization problem in which final time, energy consumption, and miss distance are treated as competing objectives. A Tabu search is utilized to build a library of initial feasible solutions for the multi-objective optimization algorithm. Additionally, a hierarchical clustering technique is utilized to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. A fuzzy-based system is employed to extract the best compromise solution over the trade-off curve.

The invention belongs to the field of machine learning, and discloses an urban rail transit passenger flow volume prediction method based on A-LSTM. The method relates to three parts of time feature extraction, spatial feature extraction and prediction algorithm design. Wherein the time characteristics are mainly characterized in that clustering analysis is carried out on the week factors througha hierarchical clustering method, and relationships among the weeks are searched; Wherein the spatial characteristics refer to passenger flow distribution characteristics of different subway stations,and the spatial passenger flow distribution relation is searched by analyzing the different stations; The prediction algorithm is mainly based on an improved LSTM neural network, and an attention mechanism is added, so that the LSTM network pays more attention to input characteristics with greater influence on prediction by a model, and passenger flow data can be predicted more accurately.

The invention relates to a short-time traffic flow prediction method considering spatial-temporal correlation. The influence of temporal correlation on the traffic flow of a target detection point is considered, and a short-time traffic flow temporal correlation prediction value is acquired; the spatial correlation of the object traffic flow is analyzed and researched by using a hierarchical clustering method, and multiple key spatial correlation points are determined; the influence of the traffic flow of the spatial correlation points on the traffic flow of the target detection point is considered, and a short-time traffic flow spatial correlation prediction value is acquired; the temporal correlation prediction value, the spatial correlation prediction value and the prediction value of the present method are integrated by using an "entropy method" so that the final prediction result of the short-time traffic flow of the target detection point is generated; and the prediction error is evaluated and analyzed according to the prediction result of the traffic flow and the actual traffic data. According to the method, the defect of the present method that the spatial-temporal characteristics cannot be fully utilized can be overcome, and the spatial-temporal correlation prediction result and the prediction result of the present method can be further integrated so that the accuracy of the short-time traffic flow prediction result can be effectively enhanced.