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This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied. [ABSTRACT FROM AUTHOR]

• Interpolative coding is efficient at chain code compression. • Interpolative coding is improved with Golomb coding. • Proper selection of Golomb parameter yields slightly better compression. A lossless chain code compression algorithm consisting of Burrows-Wheeler Transform, Move-To-Front Transform, and a modified interpolative coding is presented in this paper. The interpolative coding divides a strictly increasing array of integers and encodes the middle element recursively, where the required number of bits is obtained from the difference of the array's border values. In this paper, it is shown that, for some intervals, Golomb coding is more efficient than the interpolative coding. This fact was used to construct a hybrid approach that yields slightly better compression results than the state-of-the-art while compressing Three OrThogonal chain code, Freeman chain code in four and eight directions, and Vertex Chain Code. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an agglomerative hierarchical clustering algorithm for spatial data. It discovers clusters of arbitrary shapes which may be nested. The algorithm uses a sweeping approach consisting of three phases: sorting is done during the preprocessing phase, determination of clusters is performed during the sweeping phase, and clusters are adjusted during the post processing phase. The properties of the algorithm are demonstrated by examples. The algorithm is also adapted to the streaming algorithm for clustering large spatial datasets. [Copyright &y& Elsevier]