Tree-based partition querying: a methodology for computing medoids in large spatial datasets

Besides traditional domains (e.g., resource allocation, data mining applications), algorithms for medoid computation and related problems will play an important role in numerous emerging fields, such as location based services and sensor networks. Since the k-medoid problem is NP hard, all existing work deals with approximate solutions on relatively small datasets. This paper aims at efficient methods for very large spatial databases, motivated by: (i) the high and ever increasing availability of spatial data, and (ii) the need for novel query types and improved services. The proposed solutions exploit the intrinsic grouping properties of a data partition index in order to read only a small part of the dataset. Compared to previous approaches, we achieve results of comparable or better quality at a small fraction of the CPU and I/O costs (seconds as opposed to hours, and tens of node accesses instead of thousands). In addition, we study medoid-aggregate queries, where k is not known i...

• Type: Conference
• Year: 2008
• Where: VLDB
• Authors: Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou
• Keywords: Average Distance, Database, Intrinsic Grouping Properties, Large Spatial Databases, VLDB 2008