USA, Washington, pp 583–592Ĭhen X (2013) Clustering based on a near neighbor graph and a grid cell graph. Plenum Press, New Yorkīöhm C, Plant C, Shao J, et al. In: Proceedings of ACM SIGMOD, pp 49–60īezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. In: Proceedings of ACM SIGMOD, pp 94–105Īnkerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: Ordering points to identify the clustering structure. At last, it gives several solid and insightful future research suggestions.Īgrawal R, Gehrke J, Gunopolos D et al (1998) Automatic subspace clustering of high dimensional data for data mining application. Extensive comparison experiments with some class clustering algorithms demonstrate that our two algorithms can often get acceptable clustering results in many cases. By some simulated experiments, we observe that IESynC algorithm can get some improvement of time cost than ESynC algorithm in some data sets. We also introduce an Improved ESynC algorithm (IESynC algorithm) in time cost by combining multidimensional grid partitioning method and Red-Black tree structure.
By some simulated experiments of some artificial data sets, eight UCI data sets, and three picture data sets, we observe that ESynC algorithm not only gets better local synchronization effect but also needs less iterative times and time cost than SynC algorithm. After some analysis and experimental comparison, we observe that ESynC algorithm based on the linear version of Vicsek model can get better local synchronization effect than SynC algorithm based on an extensive Kuramoto model and a similar synchronization clustering algorithm based on the original version of Vicsek model. The development of ESynC algorithm is inspired by Synchronization Clustering (SynC) algorithm and Vicsek model. This paper presents an Effective Synchronization Clustering (ESynC) algorithm using a linear version of Vicsek model. subplots ( 2, 2, sharex = True, sharey = True ) # Create figure number 10 with a single subplot # and clears it if it already exists.
subplots ( 2, 2, sharex = 'all', sharey = 'all' ) # Note that this is the same as plt. subplots ( 2, 2, sharey = 'row' ) # Share both X and Y axes with all subplots plt. subplots ( 2, 2, sharex = 'col' ) # Share a Y axis with each row of subplots plt.
scatter ( x, y ) # Share a X axis with each column of subplots plt. subplots ( 2, 2, subplot_kw = dict ( projection = "polar" )) axs. scatter ( x, y ) # Create four polar axes and access them through the returned array fig, axs = plt. set_title ( 'Simple plot' ) # Create two subplots and unpack the output array immediately f, ( ax1, ax2 ) = plt. sin ( x ** 2 ) # Create just a figure and only one subplot fig, ax = plt. Typical idioms for handling the return value are: Theĭimensions of the resulting array can be controlled with the squeeze **fig_kwĪll additional keyword arguments are passed to theĪx can be either a single Axes object or anĪrray of Axes objects if more than one subplot was created. If False, no squeezing at all is done: the returned Axes object isĪlways a 2D array containing Axes instances, even if it ends upĭict with keywords passed to the GridSpecĬonstructor used to create the grid the subplots are placed on.for NxM, subplots with N>1 and M>1 are returned as a 2D array.for Nx1 or 1xM subplots, the returned object is a 1D numpy.Resulting single Axes object is returned as a scalar. if only one subplot is constructed (nrows=ncols=1), the.If True, extra dimensions are squeezed out from the returned.When subplots have a shared axis that has units, calling Have a shared y-axis along a row, only the y tick labels of the firstĬolumn subplot are created. Labels of the bottom subplot are created. When subplots have a shared x-axis along a column, only the x tick 'col': each subplot column will share an x- or y-axis.'row': each subplot row will share an x- or y-axis.False or 'none': each subplot x- or y-axis will be independent.True or 'all': x- or y-axis will be shared among all subplots.sharex, sharey bool or, default: FalseĬontrols sharing of properties among x ( sharex) or y ( sharey) Number of rows/columns of the subplot grid. Subplots, including the enclosing figure object, in a single call. This utility wrapper makes it convenient to create common layouts of subplots ( nrows = 1, ncols = 1, *, sharex = False, sharey = False, squeeze = True, subplot_kw = None, gridspec_kw = None, ** fig_kw ) ¶
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