Iterative refinement clustering algorithms are widely used in data mining area, but they are sensitive to the initialization. In the past decades, many modified initialization methods have been proposed to reduce the influence of initialization sensitivity problem. The essence of iterative refinement clustering algorithms is the local search method. The big numbers of the local minimum points w...

We present new iterative methods based on refinement process for solving large sparse Sylvesterobserver equations applied in state estimation of a continuous-time system. These methods use projection methods to produce low-dimensional Sylvester-observer matrix equations that are solved by the direct methods. Moreover, the refinement process described in this paper has the capability of improvin...

In this paper, a Job shop scheduling problem with a parallel assembly stage and Lot Streaming (LS) is considered for the first time in both machining and assembly stages. Lot Streaming technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. Hence, to solve job shop scheduling problem with a parallel assembly stage and lot streaming, deci...

This paper presents a performance comparison of sparse linear solvers based on iterative and direct methods for power system applications implemented on parallel computing platform. The iterative sparse linear solver evaluated in this paper is based on the conjugate gradient algorithm with a Jacobi pre-conditioner executed on a general purpose graphic processing unit (GPGPU). The direct solvers...

This paper presents a method of shape chartification suitable for surface approximation. The innovation of this approach lies on the definition of an iterative refinement of the shape into a set of patches that are automatically tiled and used to approximate the original shape up to a prescribed error. The coding of the patches is supported by the Reeb graph and contains the rules to properly t...

We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality. Therefore, traditional transforms and metrics cannot be used for data organ...

Given a white-box component C with specified unsafe states, we address the problem of automatically generating an interface that captures safe orderings of invocations of C ’s public methods. Method calls in the generated interface are guarded by constraints on their parameters. Unlike previous work, these constraints are generated automatically through an iterative refinement process. Our tech...

We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images to extract fragments that contain common object parts. We then show how these parts can be segmented into their figure and ground regions in an automatic learning process. This is in contrast with previous approaches, which req...