2014 ModelingHumanLocationDatawithMi

• (Lichman & Smyth, 2014) ⇒ Moshe Lichman, and Padhraic Smyth. (2014). “Modeling Human Location Data with Mixtures of Kernel Densities.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623681

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Modeling+Human+Location+Data+with+Mixtures+of+Kernel+Densities
• http://dl.acm.org/citation.cfm?id=2623330.2623681&preflayout=flat#citedby

Quotes

Author Keywords

• Anomaly/novelty detection; data mining; kernel density estimation; probabilistic methods; social media; spatial; spatial databases and gis; user modeling

Abstract

Location-based data is increasingly prevalent with the rapid increase and adoption of mobile devices. In this paper we address the problem of learning spatial density models, focusing specifically on individual-level data. Modeling and predicting a spatial distribution for an individual is a challenging problem given both (a) the typical sparsity of data at the individual level and (b) the heterogeneity of spatial mobility patterns across individuals. We investigate the application of kernel density estimation (KDE) to this problem using a mixture model approach that can interpolate between an individual's data and broader patterns in the population as a whole. The mixture-KDE approach is evaluated on two large geolocation / check-in data sets, from Twitter and Gowalla, with comparisons to non-KDE baselines, using both log-likelihood and detection of simulated identity theft as evaluation metrics. Our experimental results indicate that the mixture-KDE method provides a useful and accurate methodology for capturing and predicting individual-level spatial patterns in the presence of noisy and sparse data.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ModelingHumanLocationDatawithMi	Padhraic Smyth Moshe Lichman			Modeling Human Location Data with Mixtures of Kernel Densities				10.1145/2623330.2623681		2014