Time-Series Similarity Problems and Well-Separated Geometric Sets

Given a pair of nonidentical complex objects, de ning and determining how similar they are to each other is a nontrivial problem. In data mining applications, one frequently needs to determine the similarity between two time series. We analyze a model of time-series similarity that allows outliers, and di erent scaling functions. We present deterministic and randomized algorithms for computing this notion of similarity. The algorithms are based on nontrivial tools and methods from computational geometry. In particular, we use properties of families of well-separated geometric sets. The randomized algorithm has provably good performance and also works extremely e ciently in practice.

• Type: Conference
• Year: 1997
• Where: COMPGEOM
• Authors: Béla Bollobás, Gautam Das, Dimitrios Gunopulos, Heikki Mannila
• Keywords: COMPGEOM 1997, Discrete Geometry, Nonidentical Complex Objects, Nontrivial Problem, Randomized Algorithm