9,080 research outputs found
Substructure Discovery Using Minimum Description Length and Background Knowledge
The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the original data and represent structural concepts in the data. By
replacing previously-discovered substructures in the data, multiple passes of
SUBDUE produce a hierarchical description of the structural regularities in the
data. SUBDUE uses a computationally-bounded inexact graph match that identifies
similar, but not identical, instances of a substructure and finds an
approximate measure of closeness of two substructures when under computational
constraints. In addition to the minimum description length principle, other
background knowledge can be used by SUBDUE to guide the search towards more
appropriate substructures. Experiments in a variety of domains demonstrate
SUBDUE's ability to find substructures capable of compressing the original data
and to discover structural concepts important to the domain. Description of
Online Appendix: This is a compressed tar file containing the SUBDUE discovery
system, written in C. The program accepts as input databases represented in
graph form, and will output discovered substructures with their corresponding
value.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
VoG: Summarizing and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple
sentences? How can we measure the "importance" of a set of discovered subgraphs
in a large graph? These are exactly the problems we focus on. Our main ideas
are to construct a "vocabulary" of subgraph-types that often occur in real
graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the
most succinct description of a graph in terms of this vocabulary. We measure
success in a well-founded way by means of the Minimum Description Length (MDL)
principle: a subgraph is included in the summary if it decreases the total
description length of the graph.
Our contributions are three-fold: (a) formulation: we provide a principled
encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop
\method, an efficient method to minimize the description cost, and (c)
applicability: we report experimental results on multi-million-edge real
graphs, including Flickr and the Notre Dame web graph.Comment: SIAM International Conference on Data Mining (SDM) 201
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
{VoG}: {Summarizing} and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a "vocabulary" of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a well-founded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. Our contributions are three-fold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop \method, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multi-million-edge real graphs, including Flickr and the Notre Dame web graph
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
A graph theoretic approach is proposed for object shape representation in a
hierarchical compositional architecture called Compositional Hierarchy of Parts
(CHOP). In the proposed approach, vocabulary learning is performed using a
hybrid generative-descriptive model. First, statistical relationships between
parts are learned using a Minimum Conditional Entropy Clustering algorithm.
Then, selection of descriptive parts is defined as a frequent subgraph
discovery problem, and solved using a Minimum Description Length (MDL)
principle. Finally, part compositions are constructed by compressing the
internal data representation with discovered substructures. Shape
representation and computational complexity properties of the proposed approach
and algorithms are examined using six benchmark two-dimensional shape image
datasets. Experiments show that CHOP can employ part shareability and indexing
mechanisms for fast inference of part compositions using learned shape
vocabularies. Additionally, CHOP provides better shape retrieval performance
than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV
2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp
566-581. Supplementary material can be downloaded from
http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd
The Structure of Halos: Implications for Group and Cluster Cosmology
The dark matter halo mass function is a key repository of cosmological
information over a wide range of mass scales, from individual galaxies to
galaxy clusters. N-body simulations have established that the
friends-of-friends (FOF) mass function has a universal form to a surprising
level of accuracy (< 10%). The high-mass tail of the mass function is
exponentially sensitive to the amplitude of the initial density perturbations,
the mean matter density parameter, Omega_m, and to the dark energy controlled
late-time evolution of the density field. Observed group and cluster masses,
however, are usually stated in terms of a spherical overdensity (SO) mass which
does not map simply to the FOF mass. Additionally, the widely used halo models
of structure formation -- and halo occupancy distribution descriptions of
galaxies within halos -- are often constructed exploiting the universal form of
the FOF mass function. This again raises the question of whether FOF halos can
be simply related to the notion of a spherical overdensity mass. By employing
results from Monte Carlo realizations of ideal Navarro-Frenk-White (NFW) halos
and N-body simulations, we study the relationship between the two definitions
of halo mass. We find that the vast majority of halos (80-85%) in the
mass-range 10^{12.5}-10^{15.5} M_sun/h indeed allow for an accurate mapping
between the two definitions (~ 5%), but only if the halo concentrations are
known. Nonisolated halos fall into two broad classes: those with complex
substructure that are poor fits to NFW profiles and those ``bridged'' by the
(isodensity-based)FOF algorithm. A closer investigation of the bridged halos
reveals that the fraction of these halos and their satellite mass distribution
is cosmology dependent. (abridged)Comment: Submitted to Ap
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