MetricMap: an embedding technique for processing distance-based queries in metric spaces.
In this paper, we present an embedding technique, called MetricMap, which is capable of estimating distances in a pseudometric space. Given a database of objects and a distance function for the objects, which is a pseudometric, we map the objects to vectors in a pseudo-Euclidean space with a reasonably low dimension while preserving the distance between two objects approximately. Such an embedding technique can be used as an approximate oracle to process a broad class of distance-based queries. It is also adaptable to data mining applications such as data clustering and classification. We present the theory underlying MetricMap and conduct experiments to compare MetricMap with other methods including MVP-tree and M-tree in processing the distance-based queries. Experimental results on both protein and RNA data show the good performance and the superiority of MetricMap over the other methods.
Wang
Jason T L
JT
Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. wangj@njit.edu
Wang
Xiong
X
Shasha
Dennis
D
Zhang
Kaizhong
K
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D012815:Signal Processing, Computer-Assisted