Summary LNAI 3127 Concept Based Data Mining with Scaled Labeled Graphs 1st Edition by Bernhard Ganter, Peter Grigoriev, Sergei Kuznetsov, Mikhail Samokhin ISBN X - PDF Download

LNAI 3127 Concept Based Data Mining with Scaled
Labeled Graphs 1st Edition by Bernhard Ganter,
Peter Grigoriev, Sergei Kuznetsov, Mikhail
Samokhin ISBN 9783540206460 354020646X pdf
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peter-grigoriev-sergei-kuznetsov-mikhail-samokhin-
isbn-9783540206460-354020646x-13116/




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, Concept-Based Data Mining with Scaled
Labeled Graphs

Bernhard Ganter, Peter A. Grigoriev, Sergei O. Kuznetsov, and
Mikhail V. Samokhin

Technische Universität Dresden
All-Russian Institute for Scientific and Technical Information



Abstract. Graphs with labeled vertices and edges play an important
role in various applications, including chemistry. A model of learning
from positive and negative examples, naturally described in terms of For-
mal Concept Analysis (FCA), is used here to generate hypotheses about
biological activity of chemical compounds. A standard FCA technique
is used to reduce labeled graphs to object-attribute representation. The
major challenge is the construction of the context, which can involve ten
thousands attributes. The method is tested against a standard dataset
from an ongoing international competition called Predictive Toxicology
Challenge (PTC).


1 Introduction
In [1] we introduced a general construction based on a semilattice of object
description, which we called pattern structure. An example that we used was
related to a lattice on sets of labeled graphs. In general, pattern structures are
naturally reduced to formal contexts. In this paper we present a practical data
mining approach which uses JSM or concept-based hypotheses. On the data side
we use a standard FCA technique, called ordinal scaling [2] for the reduction of
labeled graphs to formal contexts. We consider a chemical application in Predic-
tive Toxicology and compare the results to those obtained with the same learning
model, but different representation language which used predefined descriptors
(attributes) for describing chemical compounds.


2 A Learning Model
2.1 Pattern Structures
In [1] we showed how such an approach is linked to the general FCA frame-
work [2]. In [3] and in [4] we showed how this approach is related to standard
machine learning models such as version spaces and decision trees.
Let G be some set, let (D,
) be a meet-semilattice and let δ : G → D be a
mapping. Then (G, D, δ) with D = (D,
) is called a pattern structure, provided
that the set
δ(G) := {δ(g) | g ∈ G}

K.E. Wolff et al. (Eds.): ICCS 2004, LNAI 3127, pp. 94–108, 2004.


c Springer-Verlag Berlin Heidelberg 2004