k-Shape

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we present k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance…

• UD
  U.S. Department of the Interior
• MR
  Microsoft Research
• IC
  IBM Center for the Business of Government
• AS
  Alexander S. Onassis Public Benefit Foundation
• IB
  Interior Business Center