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» Clustering Improves the Exploration of Graph Mining Results
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JCIT
2008
165views more  JCIT 2008»
14 years 11 months ago
Mining Processes using cluster approach for representing workflows
Although much attention is being paid to business Intelligence during the past decades, the design for applying business Intelligence and particularly in a workflow processes is s...
N. P. Kavya, M. V. Sathyanarayana, N. C. Naveen
PVLDB
2010
146views more  PVLDB 2010»
14 years 6 months ago
HaLoop: Efficient Iterative Data Processing on Large Clusters
The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing pl...
Yingyi Bu, Bill Howe, Magdalena Balazinska, Michae...
TCSV
2008
125views more  TCSV 2008»
14 years 11 months ago
Exploring Co-Occurence Between Speech and Body Movement for Audio-Guided Video Localization
This paper presents a bottom-up approach that combines audio and video to simultaneously locate individual speakers in the video (2-D source localization) and segment their speech ...
H. Vajaria, S. Sarkar, R. Kasturi
DASFAA
2007
IEEE
199views Database» more  DASFAA 2007»
15 years 6 months ago
Detection and Visualization of Subspace Cluster Hierarchies
Subspace clustering (also called projected clustering) addresses the problem that different sets of attributes may be relevant for different clusters in high dimensional feature sp...
Elke Achtert, Christian Böhm, Hans-Peter Krie...
BMCBI
2008
121views more  BMCBI 2008»
14 years 12 months ago
Microarray data mining using landmark gene-guided clustering
Background: Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of ...
Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun C...