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JIIS
2006
113views more  JIIS 2006»
13 years 8 months ago
Spatial ordering and encoding for geographic data mining and visualization
: Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a ch...
Diansheng Guo, Mark Gahegan
PAKDD
2004
ACM
186views Data Mining» more  PAKDD 2004»
14 years 1 months ago
CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees
Abstract. Tree structures are used extensively in domains such as computational biology, pattern recognition, XML databases, computer networks, and so on. One important problem in ...
Yun Chi, Yirong Yang, Yi Xia, Richard R. Muntz
AIME
2005
Springer
14 years 1 months ago
Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System
We have developed an algorithm for triaging acute pediatric abdominal pain in the Emergency Department using the discovery-driven approach. This algorithm is embedded into the MET-...
Jerzy Blaszczynski, Ken Farion, Wojtek Michalowski...
AUSDM
2006
Springer
100views Data Mining» more  AUSDM 2006»
13 years 12 months ago
Data Mining Methodological Weaknesses and Suggested Fixes
Predictive accuracy claims should give explicit descriptions of the steps followed, with access to the code used. This allows referees and readers to check for common traps, and t...
John H. Maindonald
IFIP12
2007
13 years 9 months ago
Clustering Improves the Exploration of Graph Mining Results
Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we search for (connected) subgraphs contained in many of these graphs. Each graph ca...
Edgar H. de Graaf, Joost N. Kok, Walter A. Kosters