INTELLIGENT ASSISTANCE FOR KDD-PROCESS ORIENTATION / ASSISTÊNCIA INTELIGENTE À ORIENTAÇÃO DO PROCESSO DE DESCOBERTA DE CONHECIMENTO EM BASES DE DADOS

AUTOR(ES)
DATA DE PUBLICAÇÃO

2003

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 &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 &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 e

ASSUNTO(S)

knowledge discovery in databases assistencia a definicao de tarefas de kdd data mining kdd task definition assistance mineracao de dados descoberta de conhecimento em bancos de dados planning in kdd planejamento em kdd

ACESSO AO ARTIGO

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