2009 DynaMMoMiningandSummarizationof

• (Li et al., 2009) ⇒ Lei Li, James McCann, Nancy S. Pollard, and Christos Faloutsos. (2009). “DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557078

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database applications: Data mining I.2.6 Artificial Intelligence: Learning - parameter learning.
• General Terms: Algorithms; Experimentation.

Cited By

• http://scholar.google.com/scholar?q=%22DynaMMo%3A+mining+and+summarization+of+coevolving+sequences+with+missing+values%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557078&preflayout=flat#citedby

Quotes

Author Keywords

Time Series; Missing Value; Bayesian Network; Expectation Maximization (EM).

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 DynaMMoMiningandSummarizationof	Christos Faloutsos Lei Li James McCann Nancy S. Pollard			DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values				10.1145/1557019.1557078