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113,529
An Algorithm for Tracking Multiple Targets
 IEEE Transactions on Automatic Control
, 1979
"... Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m[~clngreports, and ..."
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Cited by 596 (0 self)
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Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m
Survey of clustering algorithms
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
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Cited by 499 (4 self)
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, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts
Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
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Cited by 532 (5 self)
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Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join
The NewReno Modification to TCP’s Fast Recovery Algorithm
, 2003
"... RFC 2581 [RFC2581] documents the following four intertwined TCP congestion control algorithms: Slow Start, Congestion Avoidance, Fast Retransmit, and Fast Recovery. RFC 2581 [RFC2581] explicitly allows certain modifications of these algorithms, including modifications that use the TCP Selective Ackn ..."
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Cited by 600 (9 self)
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Acknowledgement (SACK) option [RFC2018], and modifications that respond to "partial acknowledgments" (ACKs which cover new data, but not all the data outstanding when loss was detected) in the absence of SACK. The NewReno mechanism described in this document describes a specific algorithm for responding
Virtual clock: A new traffic control algorithm for packet switching networks
 In Proc. ACM SIGCOMM
, 1990
"... A challenging research issue in high speed networking is how to control the transmission rate of statistical data P OWS. This paper describes a new algorithm, VirtualClock, for data trafic control in highspeed networks. VirtualClock maintains the statistical multiplexing flexibility of packet swit ..."
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Cited by 617 (4 self)
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A challenging research issue in high speed networking is how to control the transmission rate of statistical data P OWS. This paper describes a new algorithm, VirtualClock, for data trafic control in highspeed networks. VirtualClock maintains the statistical multiplexing flexibility of packet
A review of image denoising algorithms, with a new one
 SIMUL
, 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
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Cited by 508 (6 self)
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The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding
Bottomup computation of sparse and Iceberg CUBE
 In Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP, DOLAP ’02
, 1999
"... We introduce the IcebergCUBE problem as a reformulation of the datacube (CUBE) problem. The IcebergCUBE problem is to compute only those groupby partitions with an aggregate value (e.g., count) above some minimum support threshold. The result of IcebergCUBE can be used (1) to answer groupby que ..."
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Cited by 187 (4 self)
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CUBE computation. BUC builds the CUBE bottomup; i.e., it builds the CUBE by starting from a groupby on a single attribute, then a groupby on a pair of attributes, then a groupby on three attributes, and so on. This is the opposite of all techniques proposed earlier for computing the CUBE, and has an important
Fast Algorithms for Mining Association Rules
, 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
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Cited by 3612 (15 self)
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We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 727 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Results 11  20
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113,529