175 research outputs found
Frequent Subgraph Mining in Outerplanar Graphs
In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset
Frequent Subgraph Mining in Outerplanar Graphs
In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset
Using Echo State Networks for Cryptography
Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob share a copy of an echo state network. If Alice trains her copy
to memorize a message, she can communicate the trained part of the network to
Bob who plugs it into his copy to regenerate the message. Considering a
byte-level representation of in- and output, the technique applies to arbitrary
types of data (texts, images, audio files, etc.) and practical experiments
reveal it to satisfy the fundamental cryptographic properties of diffusion and
confusion.Comment: 8 pages, ICANN 201
10471 Abstracts Collection -- Scalable Visual Analytics
From 21.11. to 26.11.2010, the Dagstuhl Seminar 10471 ``Scalable Visual Analytics\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Maximal Closed Set and Half-Space Separations in Finite Closure Systems
Several problems of artificial intelligence, such as predictive learning,
formal concept analysis or inductive logic programming, can be viewed as a
special case of half-space separation in abstract closure systems over finite
ground sets. For the typical scenario that the closure system is given via a
closure operator, we show that the half-space separation problem is
NP-complete. As a first approach to overcome this negative result, we relax the
problem to maximal closed set separation, give a greedy algorithm solving this
problem with a linear number of closure operator calls, and show that this
bound is sharp. For a second direction, we consider Kakutani closure systems
and prove that they are algorithmically characterized by the greedy algorithm.
As a first special case of the general problem setting, we consider Kakutani
closure systems over graphs, generalize a fundamental characterization result
based on the Pasch axiom to graph structured partitioning of finite sets, and
give a sufficient condition for this kind of closures systems in terms of graph
minors. For a second case, we then focus on closure systems over finite
lattices, give an improved adaptation of the greedy algorithm for this special
case, and present two applications concerning formal concept and subsumption
lattices. We also report some experimental results to demonstrate the practical
usefulness of our algorithm.Comment: An early version of this paper was presented at ECML/PKDD 2019 and
has appeared in the Lecture Notes in Computer Science, Machine Learning and
Knowledge Discovery in Databases - European Conference, ECML PKDD 201
From movement tracks through events to places : extracting and characterizing significant places from mobility data
Best VAST 2011 paperInternational audienceWe propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales
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