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2,462
A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks
, 1997
"... We present a new distributed routing protocol for mobile, multihop, wireless networks. The protocol is one of a family of protocols which we term "link reversal" algorithms. The protocol's reaction is structured as a temporallyordered sequence of diffusing computations; each computat ..."
Abstract

Cited by 1100 (6 self)
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We present a new distributed routing protocol for mobile, multihop, wireless networks. The protocol is one of a family of protocols which we term "link reversal" algorithms. The protocol's reaction is structured as a temporallyordered sequence of diffusing computations; each
Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach
 DATA MINING AND KNOWLEDGE DISCOVERY
, 2004
"... Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still co ..."
Abstract

Cited by 1752 (64 self)
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databases, which dramatically reduces the search space. Our performance
study shows that the FPgrowth method is efficient and scalable for mining both long and short frequent patterns,
and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported
new
ROCK: A Robust Clustering Algorithm for Categorical Attributes
 In Proc.ofthe15thInt.Conf.onDataEngineering
, 2000
"... Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than point ..."
Abstract

Cited by 446 (2 self)
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Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than
On visible surface generation by a priori tree structures
 Computer Graphics
, 1980
"... This paper describes a new algorithm for solving the hidden surface (or line) problem, to more rapidly generate realistic images of 3D scenes composed of polygons, and presents the development of theoretical foundations in the area as well as additional related algorithms. As in many applications t ..."
Abstract

Cited by 370 (6 self)
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;quot;ninary space partitioning " tree whose inorder traversal of visibility priority at runtime will produce a lineaL " order, dependent upon the viewing position, on (parts of) the polygons, which can then be used to easily solve the hidden surfac6 problem. In the application where the entire
Study Of Reverberation Time Series And Echo Signal Detection In Reverberation Limited Scenarios
"... �Abstract — An innovative approach to the generation of reverberation time series and echo detection algorithms is presented The time series approach utilizes recent developments in linear spectral prediction research in which the spectra of stochastic process are modelled as rational functions and ..."
Interprocedural Compilation of Fortran D for MIMD DistributedMemory Machines
 COMMUNICATIONS OF THE ACM
, 1992
"... Algorithms exist for compiling Fortran D for MIMD distributedmemory machines, but are significantly restricted in the presence of procedure calls. This paper presents interprocedural analysis, optimization, and code generation algorithms for Fortran D that limit compilation to only one pass over ea ..."
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Cited by 333 (49 self)
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each procedure. This is accomplished by collecting summary information after edits, then compiling procedures in reverse topological order to propagate necessary information. Delaying instantiation of the computation partition, communication, and dynamic data decomposition is key to enabling
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
Abstract

Cited by 334 (2 self)
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be interpreted as recursive ℓ1norm penalized regression. Our second algorithm, based on Nesterov’s first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright
A Minmax Cut Algorithm for Graph Partitioning and Data Clustering
, 2001
"... An important application of graph partitioning is data clustering using a graph model  the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. Here we propose a new algorithm for graph partition with an objec ..."
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Cited by 213 (15 self)
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are derived. The minmax cut algorithm is tested on newsgroup datasets and is found to outperform other current popular partitioning/clustering methods. The linkagebased re nements in the algorithm further improve the quality of clustering substantially. We also demonstrate that the linearized search order
Clique Partitions, Graph Compression and Speedingup Algorithms
 Journal of Computer and System Sciences
, 1991
"... We first consider the problem of partitioning the edges of a graph G into bipartite cliques such that the total order of the cliques is minimized, where the order of a clique is the number of vertices in it. It is shown that the problem is NPcomplete. We then prove the existence of a partition of s ..."
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Cited by 88 (3 self)
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of small total order in a sufficiently dense graph and devise an efficient algorithm to compute such a partition. It turns out that our algorithm exhibits a tradeoff between the total order of the partition and the running time. Next, we define the notion of a compression of a graph G and use the result
Results 1  10
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