<XML><RECORDS><RECORD><REFERENCE_TYPE>0</REFERENCE_TYPE><REFNUM>7716</REFNUM><AUTHORS><AUTHOR>Morrison,A.J.</AUTHOR><AUTHOR>Chalmers,M.</AUTHOR></AUTHORS><YEAR>2004</YEAR><TITLE>A Pivot-Based Routine for Improved Parent-Finding in Hybrid MDS </TITLE><PLACE_PUBLISHED>Information Visualization 3(2) </PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><PAGES>109-122</PAGES><LABEL>Morrison:2004:7716</LABEL><ABSTRACT>The problem of exploring or visualising data of high dimensionality is central to many tools for information visualisation. Through representing a data set in terms of inter-object proximities, multidimensional scaling may be employed to generate a configuration of objects in low-dimensional space in such a way as to preserve high-dimensional relationships. An algorithm is presented here for a heuristic hybrid model for the generation of such configurations. Building on a model introduced in 2002, the algorithm functions by means of sampling, spring model and interpolation phases. The most computationally complex stage of the original algorithm involved the execution of a series of nearest-neighbour searches. In this paper, we describe how the complexity of this phase has been reduced by treating all high-dimensional relationships as a set of discretised distances to a constant number of randomly selected items: pivots. In improving this computational bottleneck, the algorithmic complexity is reduced from O(N^3/2) to O(N^5/4). As well as documenting this improvement, the paper describes evaluation with a data set of 108,000 13-dimensional items and a set of 23,141 17-dimensional items. Results illustrate that the reduction in complexity is reflected in significantly improved run times and that no negative impact is made upon the quality of layout produced. </ABSTRACT></RECORD></RECORDS></XML>