7,699 research outputs found
Mining Frequent Neighborhood Patterns in Large Labeled Graphs
Over the years, frequent subgraphs have been an important sort of targeted
patterns in the pattern mining literatures, where most works deal with
databases holding a number of graph transactions, e.g., chemical structures of
compounds. These methods rely heavily on the downward-closure property (DCP) of
the support measure to ensure an efficient pruning of the candidate patterns.
When switching to the emerging scenario of single-graph databases such as
Google Knowledge Graph and Facebook social graph, the traditional support
measure turns out to be trivial (either 0 or 1). However, to the best of our
knowledge, all attempts to redefine a single-graph support resulted in measures
that either lose DCP, or are no longer semantically intuitive.
This paper targets mining patterns in the single-graph setting. We resolve
the "DCP-intuitiveness" dilemma by shifting the mining target from frequent
subgraphs to frequent neighborhoods. A neighborhood is a specific topological
pattern where a vertex is embedded, and the pattern is frequent if it is shared
by a large portion (above a given threshold) of vertices. We show that the new
patterns not only maintain DCP, but also have equally significant semantics as
subgraph patterns. Experiments on real-life datasets display the feasibility of
our algorithms on relatively large graphs, as well as the capability of mining
interesting knowledge that is not discovered in prior works.Comment: 9 page
Image classification by visual bag-of-words refinement and reduction
This paper presents a new framework for visual bag-of-words (BOW) refinement
and reduction to overcome the drawbacks associated with the visual BOW model
which has been widely used for image classification. Although very influential
in the literature, the traditional visual BOW model has two distinct drawbacks.
Firstly, for efficiency purposes, the visual vocabulary is commonly constructed
by directly clustering the low-level visual feature vectors extracted from
local keypoints, without considering the high-level semantics of images. That
is, the visual BOW model still suffers from the semantic gap, and thus may lead
to significant performance degradation in more challenging tasks (e.g. social
image classification). Secondly, typically thousands of visual words are
generated to obtain better performance on a relatively large image dataset. Due
to such large vocabulary size, the subsequent image classification may take
sheer amount of time. To overcome the first drawback, we develop a graph-based
method for visual BOW refinement by exploiting the tags (easy to access
although noisy) of social images. More notably, for efficient image
classification, we further reduce the refined visual BOW model to a much
smaller size through semantic spectral clustering. Extensive experimental
results show the promising performance of the proposed framework for visual BOW
refinement and reduction
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