Abstract
An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.
Highlights
Rapid advancements in geographic spatial information technology, generation, and collection have created exponential growth in spatial data, which has resulted in increasingly complex data structures
This paper proposes an adaptive spatial clustering algorithm (ASC) that employs both sweep-circle techniques and a dynamic threshold setting based on Gestalt theory to detect spatial clusters
ASC extends the streaming clustering technique to include large spatial data sets repeating a small number of sequential passes over objects and clustering the objects using the average memory space, where the size is a fraction of the stream length
Summary
Rapid advancements in geographic spatial information technology, generation, and collection have created exponential growth in spatial data, which has resulted in increasingly complex data structures. The proposed ASC can identify the non-homogeneous density characteristics of spatial data without the need for prior knowledge or parameters It is compatible with streaming dynamic, large-scale data found in spatial clustering. TThe sswweeeep-circle iiss aannootthheer iimmppoorrttant sswweeep-line ttechnique, where ppoints aarree iinniittiiaallllyy ssoorrtteedd aaccccoorddiinngg ttoo tthheeiirr ddiissttanncces froomm aa fifixxeedd ppoollee OO iinn tthhee ccoonnvveex hhuull oof S. The algorithm proposed in this paper utilizes the Gestalt theory and the associated definition of the dynamic adaptive threshold It can efficiently locate the adaptive clusters of arbitrary shapes and can acclimate to the uneven density characteristics of spatial data to avoid the requirements of preset global parameters, such as those necessary for DBSCAN, DENCLUE, and other algorithms [41].
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