863 results about "Hierarchical clustering" patented technology

Apparatus, systems, and methods can operate to provide efficient data clustering, data classification, and data compression. A method comprises training set of training instances can be processed to select a subset of size-1 patterns, initialize a weight of each size-1 pattern, include the size-1 patterns in classes in a model associated with the training set, and then include a set of top-k size-2 patterns in a way that provides an effective balance between local, class, and global significance patterns. A method comprises processing a dataset to compute an overall significance value of each size-2 pattern in each instance in the dataset, sort the size-2 patterns, and select the top-k size-2 patterns to be represented in clusters, which can be refined into a clustered hierarchy. A method comprises creating an uncompressed bitmap, reordering the bitmap, and compressing the bitmap. Additional apparatus, systems, and methods are disclosed.

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.

In a computer-mediated method for providing representative images, image records are classified spatio-temporally into groups. In each group, image records are partitioned into clusters and the hierarchically highest cluster is ascertained. The partitioning is between a hierarchy of feature clusters and a remainder cluster, based on a predetermined plurality of saliency features. Feature clusters each have one or more of the saliency features. The remainder cluster lacks the saliency features. Feature clusters are each exclusive of the saliency features of any higher clusters in the hierarchy and non-exclusive of the saliency features of any lower feature clusters in the hierarchy. A representative image of each group is designated from respective image records based on: the respective saliency feature of the highest cluster when the highest cluster is a feature cluster and independent of the saliency features when the highest cluster is the remainder cluster.

A method for unsupervised environmental normalization for speaker verification using hierarchical clustering is disclosed. Training data (speech samples) are taken from T enrolled (registered) speakers over any one of M channels, e.g., different microphones, communication links, etc. For each speaker, a speaker model is generated, each containing a collection of distributions of audio feature data derived from the speech sample of that speaker. A hierarchical speaker model tree is created, e.g., by merging similar speaker models on a layer by layer basis. Each speaker is also grouped into a cohort of similar speakers. For each cohort, one or more complementary speaker models are generated by merging speaker models outside that cohort. When training data from a new speaker to be enrolled is received over a new channel, the speaker model tree as well as the complementary models are updated. Consequently, adaptation to data from new environments is possible by incorporating such data into the verification model whenever it is encountered.

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 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 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 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.

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 provides a detection method of a user's electricity consumption behavior based on clustering analysis. According to the detection method, a user data warehouse is created so as to collect user's electricity consumption information, the created user data warehouse is processed to form principal component data so as to process large sample data, and the mining efficiency is improved; clustering calculation is conducted on the principal component data to obtain different user's electricity consumption modes, wherein outlier objects in the user's electricity consumption modes probably are electricity stealing users, and then electricity stealing suspected users are obtained by calculating for the outlier objects by means of the hierarchical clustering method. In the provided detection method of the user's electricity consumption behavior based on the clustering analysis, the hierarchical clustering method only needs to sequence the distance between the objects and conduct clustering according to a distance sequence, and the objects do not need to be inspected or estimated; meanwhile, the detection of the outliers and the clustering calculation are highly complementary, and good scalability is achieved.