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» Approximate Clustering on Distributed Data Streams
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ICPR
2008
IEEE
14 years 9 months ago
K-means clustering of proportional data using L1 distance
We present a new L1-distance-based k-means clustering algorithm to address the challenge of clustering high-dimensional proportional vectors. The new algorithm explicitly incorpor...
Bonnie K. Ray, Hisashi Kashima, Jianying Hu, Monin...
PODS
2008
ACM
159views Database» more  PODS 2008»
14 years 7 months ago
Approximation algorithms for clustering uncertain data
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...
Graham Cormode, Andrew McGregor
ICDE
2008
IEEE
166views Database» more  ICDE 2008»
14 years 9 months ago
A Clustered Index Approach to Distributed XPath Processing
Supporting top-k queries over distributed collections of schemaless XML data poses two challenges. While XML supports expressive query languages such as XPath and XQuery, these la...
Georgia Koloniari, Evaggelia Pitoura
JIIS
2006
147views more  JIIS 2006»
13 years 7 months ago
Mining sequential patterns from data streams: a centroid approach
In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream pro...
Alice Marascu, Florent Masseglia
ICC
2007
IEEE
203views Communications» more  ICC 2007»
14 years 2 months ago
Distributed Data Aggregation Using Clustered Slepian-Wolf Coding in Wireless Sensor Networks
—Slepian-Wolf coding is a promising distributed source coding technique that can completely remove the data redundancy caused by the spatially correlated observations in wireless...
Pu Wang, Cheng Li, Jun Zheng