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» Phenomenal Data Mining: From Data to Phenomena
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ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
15 years 8 months ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
IDEAS
2007
IEEE
128views Database» more  IDEAS 2007»
15 years 8 months ago
Streaming Random Forests
Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assu...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
ICDCS
2010
IEEE
15 years 5 months ago
Sentomist: Unveiling Transient Sensor Network Bugs via Symptom Mining
—Wireless Sensor Network (WSN) applications are typically event-driven. While the source codes of these applications may look simple, they are executed with a complicated concurr...
Yangfan Zhou, Xinyu Chen, Michael R. Lyu, Jiangchu...
GIS
2008
ACM
16 years 3 months ago
Discovering controlling factors of geospatial variables
Efficient means of determining factors controlling spatial distribution of an environmental class variable are of significant interest in Earth science. In this paper, we present ...
Tomasz F. Stepinski, Wei Ding 0003, Christoph F. E...
KDD
2009
ACM
182views Data Mining» more  KDD 2009»
16 years 2 months ago
Scalable graph clustering using stochastic flows: applications to community discovery
Algorithms based on simulating stochastic flows are a simple and natural solution for the problem of clustering graphs, but their widespread use has been hampered by their lack of...
Venu Satuluri, Srinivasan Parthasarathy