Variable Markov Oracle: A Novel Sequential Data Points Clustering Algorithm with Application to 3D Gesture Query-Matching
Cheng-i Wang, Shlomo Dubnov · 2014
In this paper a new method, Variable Markov Oracle, for clustering time series data points is proposed. Variable Markov Oracle is based on previous results of Audio Oracle, a method of fast indexing repeating sub-clips in an audio stream. The proposed method is capable of discovering natural clusters with temporal relations without specifying the number of clusters. The discovery of inherent clusters in time series data points allows the devising of an efficient algorithm for time series query-matching. The ability of discovering clusters is demonstrated with a synthetic audio example, and an application of querying 3D skeletal gesture using the query-matching algorithm based on the proposed method is experimented with comparable result to state of the art.