Clustering Spatial Data in the Presence of Obstacles

Clustering is a form of unsupervised machine learning. In this paper, we proposed the DBRS_O method to identify clusters in the presence of intersected obstacles. Without doing any preprocessing, DBRS_O processes the constraints during clustering. DBRS_O can also avoid unnecessary computations when obstacles do not affect the clustering result. As well, DBRS_O can find clusters with arbitrary shapes, varying densities, and significant non-spatial attributes in large datasets.

• Type: Conference
• Year: 2004
• Where: FLAIRS
• Authors: Xin Wang, Howard J. Hamilton
• Keywords: Artificial Intelligence, FLAIRS 2004, Intersected Obstacles, Significant Non-spatial Attributes, Unsupervised Machine