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As obras disponibilizadas nesta Biblioteca Digital foram publicadas sob expressa autorização dos respectivos autores, em conformidade com a Lei 9610/98.
A consulta aos textos, permitida por seus respectivos autores, é livre, bem como a impressão de trechos ou de um exemplar completo exclusivamente para uso próprio. Não são permitidas a impressão e a reprodução de obras completas com qualquer outra finalidade que não o uso próprio de quem imprime.
A reprodução de pequenos trechos, na forma de citações em trabalhos de terceiros que não o próprio autor do texto consultado,é permitida, na medida justificada para a compreeensão da citação e mediante a informação, junto à citação, do nome do autor do texto original, bem como da fonte da pesquisa.
A violação de direitos autorais é passível de sanções civis e penais.
Coleção Digital
Título: INTELLIGENT ASSISTANCE FOR KDD-PROCESS ORIENTATION Autor: RONALDO RIBEIRO GOLDSCHMIDT
Instituição: PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO - PUC-RIO
Colaborador(es):
EMMANUEL PISECES LOPES PASSOS - ADVISOR
MARLEY MARIA BERNARDES REBUZZI VELLASCO - CO-ADVISOR
Nº do Conteudo: 4309
Catalogação: 15/12/2003 Idioma(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Natureza: SCHOLARLY PUBLICATION
Nota: Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
Referência [pt]: https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=4309@1
Referência [en]: https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=4309@2
Referência DOI: https://doi.org/10.17771/PUCRio.acad.4309
Resumo:
Título: INTELLIGENT ASSISTANCE FOR KDD-PROCESS ORIENTATION Autor: RONALDO RIBEIRO GOLDSCHMIDT
MARLEY MARIA BERNARDES REBUZZI VELLASCO - CO-ADVISOR
Nº do Conteudo: 4309
Catalogação: 15/12/2003 Idioma(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Natureza: SCHOLARLY PUBLICATION
Nota: Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
Referência [pt]: https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=4309@1
Referência [en]: https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=4309@2
Referência DOI: https://doi.org/10.17771/PUCRio.acad.4309
Resumo:
Generally speaking, such aspects involve difficulties in
perceiving innumerable facts whose origin and levels of
detail are highly diverse and diffused, in adequately
interpreting these facts, in dynamically conjugating such
interpretations, and in deciding which actions must be
performed in order to obtain good results. How are the
objectives of the process to be identified in a precise
manner? How is one among the countless existing data mining
and preprocessing algorithms to be selected? And most
importantly, how can the selected algorithms be put to
suitable use in each different situation? These are but
a few examples of the complex and recurrent questions that
are posed when KDD processes are performed. Human analysts
must cope with the arduous task of orienting the execution
of KDD processes. To this end, in face of each different
scenario, humans resort to their previous experiences,
their knowledge, and their intuition in order to interpret
and combine the facts and therefore be able to decide on
the strategy to be adopted (Fayyad et al., 1996a, b; Wirth
et al., 1998). Although the existing computational
alternatives have proved to be useful and desirable, few of
them are designed to help humans to perform KDD processes
(Engels, 1996; Amant and Cohen, 1997; Livingston, 2001;
Bernstein et al., 2002; Brazdil et al., 2003). In
association with the above-mentioned fact, the demand for
KDD applications in several different areas has increased
dramatically in the past few years (Buchanan, 2000). Quite
commonly, the number of available practitioners with
experience in KDD is not sufficient to satisfy this growing
demand (Piatetsky-Shapiro, 1999). Within such a context,
the creation of intelligent tools that aim to assist humans
in controlling KDD processes proves to be even more
opportune (Brachman and Anand, 1996; Mitchell, 1997).
Such being the case, the objectives of this thesis were to
investigate, propose, develop, and evaluate an Intelligent
Machine for KDD-Process Orientation that is basically
intended to serve as a teaching tool to be used in
professional specialization courses in the area of
Knowledge Discovery in Databases. The basis for
formalization of the proposed machine was the Planning
Theory for Problem-Solving (Russell and Norvig, 1995) in
Artificial Intelligence. Its implementation was based on
the integration of assistance functions that are used at
different KDD process control levels: Goal Definition, KDD
Action-Planning, KDD Action Plan Execution, and Knowledge
Acquisition and Formalization. The Goal Definition
Assistant aims to assist humans in identifying KDD
tasks that are potentially executable in KDD applications.
This assistant was inspired by the detection of a certain
type of similarity between the intensional levels presented
by certain databases. The observation of this fact helps
humans to mine the type of knowledge that must be
discovered since data sets with similar structures tend to
arouse similar interests even in distinct KDD applications.
Concepts from the Theory of Attribute Equivalence in
Databases (Larson et al., 1989) make it possible to use a
common structure in which any database may be represented.
In this manner, when databases are represented in the new
structure, it is possible to map them into KDD tasks that
are compatible with such a structure. Topological space
concepts and ANN resources as described in Topological
Spaces (Lipschutz, 1979) and Artificial Neural Nets
(Haykin, 1999) have been employed so as to allow mapping
between heterogeneous patterns. After the goals have been
defined in a KDD application, it is necessary to decide how
such goals are to be achieved. The first step involves
selecting the most appropriate data mining algorithm for
the problem at hand. The KDD Action-Planning Assistant
helps humans to make this choice. To this end, it makes
use of a methodology for ordering the mining algorithms
that is based on the previous experiences, their knowledge, and their intuition in order to
interpret and combine the facts and therefore be able to decide on the strategy to
be adopted (Fayyad et al., 1996a, b; Wirth et al., 1998). Although the existing
computational alternatives have proved to be useful and desirable, few of them are
designed to help humans to perform KDD processes (Engels, 1996; Amant &
Cohen, 1997; Livingston, 2001; Bernstein et al., 2002; Brazdil et al., 2003). In
association with the above-mentioned fact, the demand for KDD applications in
several different areas has increased dramatically in the past few years (Buchanan,
2000). Quite commonly, the number of available practitioners with experience in
KDD is not sufficient to satisfy this growing demand (Piatetsky-Shapiro, 1999).
Within such a context, the creation of intelligent tools that aim to assist humans in
controlling KDD processes proves to be even more opportune (Brachman &
Anand, 1996; Mitchell, 1997).
Such being the case, the objectives of this thesis were to investigate,
propose, develop, and evaluate an Intelligent Machine for KDD-Process
Orientation that is basically intended to serve as a teaching tool to be used in
professional specialization courses in the area of Knowledge Discovery in
Databases.
The basis for formalization of the proposed machine was the Planning
Theory for Problem-Solving (Russell and Norvig, 1995) in Artificial Intelligence.
Its implementation was based on the integration of assistance functions that are
used at different KDD process control levels: Goal Definition, KDD Action-
Planning, KDD Action Plan Execution, and Knowledge Acquisition and
Formalization.
The Goal Definition Assistant aims to assist humans in identifying KDD
tasks that are potentially executable in KDD applications. This assistant was
inspired by the detection of a certain type of similarity between the intensional
levels presented by certain databases. The observation of this fact helps humans to
mine the type of knowledge that must be discovered since data sets with similar
structures tend to arouse similar interests even in distinct KDD applications.
Concepts from the Theory of Attribute Equivalence in Databases (Larson et al.,
1989) make it possible to use a common structure in which any database may be
represented. In this manner, when databases are represented in the new structure,
it is possible to map them into KDD tasks that are compatible with such a
structure. Topological space concepts and ANN resources as described in
Topological Spaces (Lipschutz, 1979) and Artificial Neural Nets (Haykin, 1999)
have been employed so as to allow mapping between heterogeneous patterns.
After the goals have been defined in a KDD application, it is necessary to
decide how such goals are to be achieved. The first step involves selecting the
most appropriate data mining algorithm for the problem at hand. The KDD
Action-Planning Assistant helps humans to make this choice. To this end, it makes
use of a methodology for ordering the mining algorithms that is based on the
previous performance of these algorithms in similar problems (Soares et al., 2001;
Brazdil et al., 2003). Algorithm ordering criteria based on database similarity at
the intensional and extensional levels were proposed, described and evaluated.
The data mining algorithm or algorithms having been selected, the next step
involves selecting the way in which data preprocessing is to be performed. Since
there is a large variety of preprocessing algorithms, many are the alternatives for
combining them (Bernstein et al., 2002). The KDD Action-Planning Assistant also
helps humans to formulate and to select the KDD action plan or plans to be
adopted. To this end, it makes use of concepts contained in the Planning Theory
for Problem-Solving.
Once a KDD action plan has been chosen, it is necessary to execute it.
Executing a KDD action plan involves the ordered execution of the KDD
algorithms that have been anticipated in the plan. Executing a KDD algorithm
requires knowledge about it. The KDD Action Plan Execution Assistant provides
specific guidance on KDD algorithms. In addition, this assistant is equipped with
mechanisms that provide specialized assistance for performing the KDD
algorithm execution process and for analyzing the results obtained. Some of these
mechanisms have been described and evaluated.
The execution of the Knowledge Acquisition and Formalization Assistant
is an operational requirement for running the proposed machine. The objective of
this assistant is to acquire knowledge about KDD and to make such knowledge
available by representing and organizing it a way that makes it possible to process
the above-mentioned assistance functions. A variety of knowledge acquisition
resources and techniques were employed in the conception of this assistant.
Descrição | Arquivo |
COVER, ACKNOWLEDGEMENTS, RESUMO, ABSTRACT, SUMMARY AND LISTS | |
CHAPTER 1 | |
CHAPTER 2 | |
CHAPTER 3 | |
CHAPTER 4 | |
CHAPTER 5 | |
CHAPTER 6 | |
CHAPTER 7 AND REFERENCES |