Geographic Data Mining and Knowledge Discovery

By Harvey J. Miller, Jiawei Han. 2001

769 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Spatial data mining and geographic knowledge discovery - An introduction (2009, neutral): “…Chawla et al., 2000; Gahegan, 2003; Guo, Peuquet, & Gahegan, 2003; Guo et al., 2006; Han, Koperski & Stefanovic, 1997; Keim et al., 2004; Knorr & Ng, 1996; Kulldorff, 1997; Mennis & Liu, 2005; Miller & Han, 2009; Miller and Han, 2001; Openshaw, Charlton, Wymer, & Craft, 1987; Shekhar et al. 2004).”
Fast app launching for mobile devices using predictive user context (2012, background): “The literature offers many geospatial clustering algorithms [10], and after experimenting with several, we chose k-means clustering which incurs low computation cost while performing comparably to the others.”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “one zone of a city to another), may be too complex to be interpreted effectively by the analyst[79].”
Emergency response after 9/11: the potential of real-time 3D GIS for quick emergency response in micro-spatial environments (2005, methodology): “These include interactive 3D geovisualization (Kwan, 2000; Kwan & Lee, 2003), multi-dimensional GIS modeling (Raper, 2000), rule-based methods, agent-based modeling, pattern recognition and spatial data mining algorithms (Abler & Richardson, 2003; Galloway, 2003; Miller & Han, 2001).”
Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice (2012, background): “Many exciting developments in data mining and knowledge discovery have been re-ported elsewhere (e.g., Miller and Han 2009), but robust methodologies for productive VGI applications should not be conﬁned to analysis alone.”
Classification Approaches in Neighborhood Research: Introduction and Review (2011, background): “Particularly where microdata are georeferenced to points, spatial clustering is one of the valuable tools available for geographic data mining and the discovery of functional territories (Miller and Han, 2001).”
Subspace clustering for high dimensional data: a review (2004, background): “More recent data mining texts include a chapter on clustering [34; 39; 45; 73].”
Graphical Models for Inference with Missing Data (2013, background): “Other areas such as data mining [7], knowledge discovery [18] and network tomography [2] are also plagued by missing data problems.”