16,139 research outputs found
Towards Distributed Convoy Pattern Mining
Mining movement data to reveal interesting behavioral patterns has gained
attention in recent years. One such pattern is the convoy pattern which
consists of at least m objects moving together for at least k consecutive time
instants where m and k are user-defined parameters. Existing algorithms for
detecting convoy patterns, however do not scale to real-life dataset sizes.
Therefore a distributed algorithm for convoy mining is inevitable. In this
paper, we discuss the problem of convoy mining and analyze different data
partitioning strategies to pave the way for a generic distributed convoy
pattern mining algorithm.Comment: SIGSPATIAL'15 November 03-06, 2015, Bellevue, WA, US
Partitioning Complex Networks via Size-constrained Clustering
The most commonly used method to tackle the graph partitioning problem in
practice is the multilevel approach. During a coarsening phase, a multilevel
graph partitioning algorithm reduces the graph size by iteratively contracting
nodes and edges until the graph is small enough to be partitioned by some other
algorithm. A partition of the input graph is then constructed by successively
transferring the solution to the next finer graph and applying a local search
algorithm to improve the current solution.
In this paper, we describe a novel approach to partition graphs effectively
especially if the networks have a highly irregular structure. More precisely,
our algorithm provides graph coarsening by iteratively contracting
size-constrained clusterings that are computed using a label propagation
algorithm. The same algorithm that provides the size-constrained clusterings
can also be used during uncoarsening as a fast and simple local search
algorithm.
Depending on the algorithm's configuration, we are able to compute partitions
of very high quality outperforming all competitors, or partitions that are
comparable to the best competitor in terms of quality, hMetis, while being
nearly an order of magnitude faster on average. The fastest configuration
partitions the largest graph available to us with 3.3 billion edges using a
single machine in about ten minutes while cutting less than half of the edges
than the fastest competitor, kMetis
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
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