DynaMMo: mining and summarization of coevolving sequences with missing values

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values. We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences. Categories and Subject Descriptors: H.2.8 Database applications: Data mining I.2.6 Artificial Intelligence: Learning - parameter learning General Terms: Algorithms; Experimentation.

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Lei Li, James McCann, Nancy S. Pollard, Christos Faloutsos
• Keywords: Data Mining, Finds Latent Variables, KDD 2009, Motion Capture Sequences, Multiple Time Sequences