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| 20110040758 | GRID-BASED DATA CLUSTERING METHOD - A grid-based data clustering method comprises: a parameter setting step, a partition step, a searching step, a seed-classifying step, an extension step, and a termination step. Through the above-mentioned steps, data in a data set are disposed in a plurality of grids, and the grids are classified into dense grids and uncrowded grids for a cluster to extend from one of the dense grid to gradually combine data in other dense grids nearby. Consequently, convenience in parameter setting, efficiency and accuracy in data clustering, and performance in noise filtering are achieved. | 02-17-2011 |
| 20110055212 | DENSITY-BASED DATA CLUSTERING METHOD - A density-based data clustering method, comprising a parameter-setting step for setting a scanning radius and a minimum threshold value, a dividing step for dividing a space of a plurality of data points according to the scanning radius, a data-retrieving step for retrieving one data point out of the plurality of data points as a core data point, a searching step for calculating a distance between the core data point and each of the query points, a grouping determination step for determining whether a number of the neighboring points is smaller than the minimum threshold value. | 03-03-2011 |
| 20110072016 | DENSITY-BASED DATA CLUSTERING METHOD - A density-based data clustering method, comprising a parameter-setting step, a first retrieving step, a first determination step, a second determination step, a second retrieving step, a third determination step and first and second termination determination steps. The parameter-setting step sets parameters. The first retrieving step retrieves one data point and defines neighboring points. The first determination step determines whether the number of the data points exceeds the minimum threshold value. The second determination step arranges a plurality of first border symbols. The second retrieving step retrieves one seed data point from the seed list, arranges a plurality of second border symbols and defines seed neighboring points. The third determination step determines whether a data point density of searching ranges of the seed neighboring points is the same. The first termination determination step determines whether the clustering is finished. The second termination determination step determines whether to finish the method steps. | 03-24-2011 |
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| 20080306715 | Detecting Method Over Network Intrusion - A detecting method over network intrusion comprises: selecting a plurality of features contained within plural statistical data by a data-transforming module; normalizing a plurality of feature values of the selected features into the same scale to obtain a plurality of normalized feature data; creating at least one feature model by a data clustering technique incorporated with density-based and grid-based algorithms through a model-creating module; evaluating the at least one feature model through a model-identifying module to select a detecting model; and detecting whether a new packet datum belongs to an intrusion instance or not by a detecting module. | 12-11-2008 |
| 20110026830 | CODEBOOK GENERATING METHOD - A codebook generating method comprises a dividing and transforming step dividing an original image into original blocks and transforming each of the original blocks into an original vector; a parameter setting step setting a distortion tolerance and a predetermined number of representative blocks; a single group setting step setting the original vectors as a group; a preliminary grouping step grouping all the original vectors in a group currently having largest distortion into two groups using a grouping algorithm, wherein the preliminary grouping step is repeated until the number of groups is equal to the predetermined number of representative blocks; and a grouping step grouping all the original vectors based on a plurality of initial centroids to obtain final centroids, and storing vectors corresponding to the final centroids in a codebook, wherein the centroids of the groups are treated as the initial centroids. | 02-03-2011 |
| 20110066580 | CODEBOOK GENERATING METHOD - A codebook generating method comprises a dividing and transforming step dividing an original image into original blocks and transforming the original blocks into original vectors; a dividing step grouping the original vectors to obtain centroids; a first layer neuron training step selecting a portion of the centroids as first-level neurons; a grouping step assigning each of the original vectors to a closest first-level neuron so as to obtain groups; a second layer neuron assigning step assigning a number of second-level neurons in each of the groups, and selecting a portion of the original vectors in each of the groups as the second-level neurons; and a second layer neuron training step defining the original vectors in each of the groups as samples, training the second-level neurons in each of the groups to obtain final neurons, and storing vectors corresponding to the final neurons in a codebook. | 03-17-2011 |
