Abstract: A method for classifying data involves receiving a set of training data from a physical process such as a computer network (20). The training data has attribute data and target data. The target data has a class label associated with the attribute data. Dummy clusters are derived from centroid coordinates of the training data associated with the class label (22). Distance measures are determined between the training data and a plurality of clusters which include the dummy clusters (24). Real clusters are created in the plurality of clusters if the training data is closest to a dummy cluster or a cluster having a class label different than the class label associated with the training data (26). A closest match between data to be classified and the plurality of clusters is identified (28) and the data is classified as the class label of the closest match from the plurality of clusters (30).

Abstract: Job scheduling techniques to reduce the variance of waiting time for stable performance delivery jobs from requesting entities such as PCs connected by a network to a resource such as a server in batches. Each batch contains N or less jobs. A first, waiting buffer can receive the requests and a second, processing buffer receives each batch from the waiting buffer. A batch of jobs to be processed are arranged by a routine called the “Yelf Spiral” in which the list of jobs is begun by placing the smallest job at the center of the list and each succeeding larger job (the next smallest) is placed alternately to the left and right of the smallest job until the largest job has been placed on the list. The jobs are then performed in the order that places the largest job last.

Abstract: A method for classifying data involves receiving a set of training data from a physical process such as a computer network (20). The training data has attribute data and target data. The target data has a class label associated with the attribute data. Dummy clusters are derived from centroid coordinates of the training data associated with the class label (22). Distance measures are determined between the training data and a plurality of clusters which include the dummy clusters (24). Real clusters are created in the plurality of clusters if the training data is closest to a dummy cluster or a cluster having a class label different than the class label associated with the training data (26). A closest match between data to be classified and the plurality of clusters is identified (28) and the data is classified as the class label of the closest match from the plurality of clusters (30).