Prof. Min Deng Profile

Prof. Min Deng

at Central South Univ

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Publications (2)

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Proceedings Article | 30 October 2009 Paper

A local distribution based spatial clustering algorithm

Min Deng, Qiliang Liu, Guangqiang Li, Tao Cheng

Proceedings Volume 7495, 74950D (2009) https://doi.org/10.1117/12.833567

KEYWORDS: Algorithm development, Databases, Computer simulations, Data mining, Detection and tracking algorithms, Spatial analysis, Distance measurement, Geomatics, Knowledge discovery, Geography

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Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential spatial association rules and outliers among the spatial data. Most existing spatial clustering algorithms only utilize the spatial distance or local density to find the spatial clusters in a spatial database, without taking the spatial local distribution characters into account, so that the clustered results are unreasonable in many cases. To overcome such limitations, this paper develops a new indicator (i.e. local median angle) to measure the local distribution at first, and further proposes a new algorithm, called local distribution based spatial clustering algorithm (LDBSC in abbreviation). In the process of spatial clustering, a series of recursive search are implemented for all the entities so that those entities with its local median angle being very close or equal are clustered. In this way, all the spatial entities in the spatial database can be automatically divided into some clusters. Finally, two tests are implemented to demonstrate that the method proposed in this paper is more prominent than DBSCAN, as well as that it is very robust and feasible, and can be used to find the clusters with different shapes.

Proceedings Article | 16 October 2009 Paper

An adaptive spatial clustering algorithm based on the minimum spanning tree-like

Min Deng, Qiliang Liu, Guangqiang Li, Tao Cheng

Proceedings Volume 7492, 74921Z (2009) https://doi.org/10.1117/12.838295

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Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential rules and outliers among the spatial data. Most existing spatial clustering methods cannot deal with the uneven density of the data and usually require predefined parameters which are hard to justify. In order to overcome such limitations, we firstly propose the concept of edge variation factor based upon the definition of distance variation among the entities in the spatial neighborhood. Then, an approach is presented to construct the minimum spanning tree-like (MST-L). Further, an adaptive MST-L based spatial clustering algorithm (AMSTLSC) is developed in this paper. The spatial clustering algorithm only involves the setting of the threshold of edge variation factor as an input parameter, which is easily made with the support of little priori information. Through this parameter, a series of MST-L can be automatically generated from the high-density region to the low-density one, where each MST-L represents a cluster. As a result, the algorithm proposed in this paper can adapt to the change of local density among spatial points. This property is also called the adaptiveness. Finally, two tests are implemented to demonstrate that the AMSTLSC algorithm is very robust and suitable to find the clusters with different shapes. Especially the algorithm has good adaptiveness. A comparative test is made to further prove the AMSTLSC algorithm better than classic DBSCAN algorithm.

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