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JMLR
2010
130views more  JMLR 2010»
14 years 4 months ago
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, P...
KDD
2010
ACM
199views Data Mining» more  KDD 2010»
15 years 1 months ago
Online discovery and maintenance of time series motifs
The detection of repeated subsequences, time series motifs, is a problem which has been shown to have great utility for several higher-level data mining algorithms, including clas...
Abdullah Mueen, Eamonn J. Keogh
AMAI
2008
Springer
14 years 10 months ago
The giving tree: constructing trees for efficient offline and online multi-robot coverage
This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multi-robot coverage algorithm should result in a coverage path for every ...
Noa Agmon, Noam Hazon, Gal A. Kaminka
SIGIR
2009
ACM
15 years 4 months ago
Dynamicity vs. effectiveness: studying online clustering for scatter/gather
We proposed and implemented a novel clustering algorithm called LAIR2, which has constant running time average for on-the-fly Scatter/Gather browsing [4]. Our experiments showed ...
Weimao Ke, Cassidy R. Sugimoto, Javed Mostafa
IMC
2007
ACM
14 years 11 months ago
Passive online rogue access point detection using sequential hypothesis testing with TCP ACK-pairs
Rogue (unauthorized) wireless access points pose serious security threats to local networks. In this paper, we propose two online algorithms to detect rogue access points using se...
Wei Wei, Kyoungwon Suh, Bing Wang, Yu Gu, Jim Kuro...