30 results about "Pattern clustering" patented technology

Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including e-Commerce target marketing, bioinformatics (large scale scientific data analysis), and automatic computing (web usage analysis), etc. However, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle datasets of thousands of records, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records and network event logs are usually modeled as data sequences. Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences. There is presented herein a novel method that offers this capability.

A generalized pattern recognition is used to identify faults in machine condition monitoring. Pattern clusters are identified in operating data. A classifier is trained using the pattern clusters in addition to annotated training data. The operating data is also used to cluster the signals in the operating data into signal clusters. Monitored data samples are then classified by evaluating confidence vectors that include substitutions of signals contained in the training data by signals in the same signal clusters as the signals contained in the training data.

Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.

The invention provides a method and device for clustering and extracting entity relationship modes. The method for clustering the entity relationship modes comprises the steps that original sentences are preprocessed so that entity words expressing entities in the original sentences are identified; the entity relationship among the entity words in the preprocessed sentences is determined according to the entity words, relational words in the relational word bodies, and special occurring sequences of the entity words and the relational words in the preprocessed sentences, and the preprocessed sentences are split into secondary sentences according to the determined entity relationship; the entity relationship modes of the split secondary sentences are extracted, wherein the entity relationship modes of the secondary sentences are expressed through relationship tuples composed of the entity words and contexts among the entity words; a first similarity among the entity relationship modes of the extracted secondary sentences is calculated; the entity relationship modes of the secondary sentences are clustered into entity relationship mode types according to the calculated first similarity among the entity relationship modes of the secondary sentences.

The invention discloses a method, system and device for online classification and evaluation of slurry quality in a desulfurization system. The method includes: collecting historical operation data of relevant parameters of the desulfurization system and corresponding slurry quality evaluation labels, obtaining an original data sample D, and performing data analysis. Cleaning; Steady-state screening with steady-state judgment conditions and standardized preprocessing; Dimensionality reduction processing for dimensionally standardized samples BD using local preservation projection LPP algorithm, and agglomerative k-means clustering method for dimensionality-reduced samples JD conducts pattern clustering and recognition, analyzes the clustering results, and obtains the slurry quality classification and evaluation library N; obtains a new sample S of relevant parameters of the desulfurization system, adds it to the steady-state operation data sample set SD for iterative calculation, and obtains a dimensionally standardized sample BDS and the sample JDS after dimensionality reduction, and pattern clustering of the sample JDS, compared with the typical sample labels of N in the classification evaluation library, to obtain the evaluation category of the new sample S.

The invention discloses a method and a device for extracting POI (Point of Interest) data from webpages, and relates to the technical field of information point extraction. The method comprises the following steps: acquiring a plurality of webpages comprising POI data; performing address mode clustering on the webpages according to the URL (Uniform Resource Locator) address of each webpage; sequencing a plurality of address modes based on the quantity of webpages corresponding to each address mode to obtain the sequencing result of each address mode; selecting N address modes with largest webpage quantities; extracting POI data comprised in the webpages corresponding to the N address modes respectively. Through the method and the device, mass webpages of POI data can be determined from hundred-billion-scale webpages more rapidly, and the POI data are extracted from the webpages more accurately.

The invention discloses a parallel network flow feature detection method based on pattern clustering, comprising the steps of: selecting a pattern matching algorithm set, selecting a pattern length segmentation point, and dividing the pattern set into a short pattern set and a long pattern set; Divide the long pattern set into multiple long pattern subsets, determine the number of processing units of the short pattern set and the long pattern subset; copy the number of copies of the text to be detected as the total number of copies of the long and short pattern subsets, and divide each copy to be tested The text is input into the processing unit corresponding to each pattern subset; combined with the processing results of each pattern subset, it is judged whether there is an attack pattern in the text to be detected; the above steps are repeated to continuously detect the data stream transmitted by the fast network stream to be detected. This method can be applied to various levels of performance requirements and pattern sets of various scales, and has extremely far-reaching effects on systems such as high-performance content detection, intrusion detection, virus protection, unified threat management, and network information monitoring.

The invention relates to a fingerprint quality evaluation method based on ridge quality expert visual cognition machine learning. Including: expert cognition and quality marking of the image quality level of stamping lines in the "on-site reconstruction area of ​​fingerprint line leftover positions", and "expert individual quality evaluation stability analysis" and "expert quality evaluation" for quality marking data. Pattern cluster analysis" and get the priority of each expert's quality marking data; cut the expert's quality marking data into pieces, and use them for image quality evaluation neural network model training according to the priority. Construct and train the neural network model until it evaluates the quality of local blocks and reaches the set accuracy threshold. Using the local block quality evaluation data made by the neural network model, the global comprehensive quality evaluation of the imprinted fingerprint image is calculated. The invention takes into account the bicuspid requirements of "multi-genre fingerprint comparison algorithm" and "expert fingerprint identification" on fingerprint quality, and is widely applicable to image quality evaluation of heterogeneous fingerprints of various specifications.

The invention discloses a method for extracting software defect repair templates based on cluster analysis, which belongs to the field of software maintenance. The steps are as follows: firstly define the fine-grained modification mode of the bug, and identify the fine-grained modification mode related to each bug; The program elements of fine-grained modification patterns related to each bug are captured; then the top-level modification pattern multiset of each bug is obtained, and then hierarchical clustering analysis is performed to obtain multiple top-level modification pattern multisets after clustering; after that, each A new modification mode multiset corresponding to a top-level modification mode multiset; then obtain the modification mode multiset map according to the relationship between program elements; then segment and optimize the modification mode multiset map to obtain modification mode clustering; finally according to the modification Pattern clustering constructs templates for software defect repair. The repair template obtained by the method of the invention has semantic features, and has stronger universality and versatility, thereby improving the efficiency and precision of defect repair.

A Space Division Multiple Access transmission method based on a statistical characteristic pattern including configuring multiple receiving and transmitting antennae at a base station side into one or more antenna arrays; comparing a diagonalization performance among the unitary matrixes for a correlation matrix of subscriber channels, with a long-time statistical correlation matrix of the subscriber channels at the base station side; calculating an optimal diagonalized correlation matrix, and then performing characteristic pattern clustering for the subscribers in a cell with the space resource divisions of the subscribers; grouping the subscribers that belong to a same characteristic pattern cluster into space division subscriber groups; carrying out SDMA transmission, by the subscribers in a same space division subscriber group, with their space resource divisions; dynamically performing subscriber characteristic pattern clustering and space division subscriber grouping described above to generate updated characteristic pattern clusters for the subscribers and updated space division subscriber groups.