Abstract: A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.

Abstract: A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.

Abstract: A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.

Abstract: The present invention is an apparatus and method for classifying high-dimensional sparse datasets. A raw data training set is flattened by converting it from categorical representation to a boolean representation. The flattened data is then used to build a class model on which new data not in the training set may be classified. In one embodiment, the class model takes the form of a decision tree, and large itemsets and cluster information are used as attributes for classification. In another embodiment, the class model is based on the nearest neighbors of the data to be classified. An advantage of the invention is that, by flattening the data, classification accuracy is increased by eliminating artificial ordering induced on the attributes. Another advantage is that the use of large itemsets and clustering increases classification accuracy.

Abstract: The present invention discloses a data mining method and apparatus that assigns weight values to items and/or transactions based on the value to the user, thereby resulting in association rules of greater importance. A conservative method, aggressive method, or a combination of the two can be used when generating supersets.

Abstract: Multidimensional similarity join finds pairs of multi-dimensional points that are within some small distance of each other. Databases in domains such as multimedia and time-series can require a high number of dimensions. The .epsilon.-k-d-B tree has been proposed as a data structure that scales better as number of dimensions increases compared to previous data structures such as the R-tree (and variations), grid-file, and k-d-B tree. We present a cost model of the .epsilon.-k-d-B tree and use it to optimize the leaf size. This new leaf size is shown to be better in most situations compared to previous work that used a constant leaf size. We present novel parallel procedures for the .epsilon.-k-d-B tree. A load-balancing strategy based on equi-depth histograms is shown to work well for uniform or low-skew situations, whereas another based on weighted, equi-depth histograms works far better for high-skew datasets. The latter strategy is only slightly slower than the former strategy for low skew datasets.

Abstract: The present invention is directed to an improved data clustering method and apparatus for use in data mining operations. The present invention determines the pattern vectors of a k-d tree structure which are closest to a given prototype cluster by pruning prototypes through geometrical constraints, before a k-means process is applied to the prototypes. For each sub-branch in the k-d tree, a candidate set of prototypes is formed from the parent of a child node. The minimum and maximum distances from any point in the child node to any prototype in the candidate set is determined. The smallest of the maximum distances found is compared to the minimum distances of each prototype in the candidate set. Those prototypes with a minimum distance greater than the smallest of the maximum distances are pruned or eliminated. Pruning the number of remote prototypes reduces the number of distance calculations for the k-means process, significantly reducing the overall computation time.