BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Signal background estimation and baseline correction algorithms for accurate DNA sequencing
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and inferring transportation routines
Artificial Intelligence
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Cellular Census: Explorations in Urban Data Collection
IEEE Pervasive Computing
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 18th international conference on World wide web
Building Extraction from Aerial Imagery Based on the Principle of Confrontation and Priori Knowledge
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 01
Querying Spatio-temporal Patterns in Mobile Phone-Call Databases
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Automated land use identification using cell-phone records
HotPlanet '11 Proceedings of the 3rd ACM international workshop on MobiArch
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Median Filtering in Constant Time
IEEE Transactions on Image Processing
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Pervasive large-scale infrastructures (like GPS, WLAN networks or cell-phone networks) generate large datasets containing human behavior information. One of the applications that can benefit from this data is the study of urban environments. In this context, one of the main problems is the detection of dense areas, i.e., areas with a high density of individuals within a specific geographical region and time period. Nevertheless, the techniques used so far face an important limitation: the definition of dense area is not adaptive and as a result the areas identified are related to a threshold applied over the density of individuals, which usually implies that dense areas are mainly identified in downtowns. In this paper, we propose a novel technique, called AdaptiveDAD, to detect dense areas that adaptively define the concept of density using the infrastructure provided by a cell phone network. We evaluate and validate our approach with a real dataset containing the Call Detail Records (CDR) of fifteen million individuals.