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Several research fields have described phenomena that produ ce endless sequences of samples, referred to as data streams. These phe nomena usually present behavior variation and are studied by means of unsupervised induction based on data clustering. In order to cope with the characteristics o f data streams, researchers have designed clustering algorithms with low time a nd space complexity requirements. However, predefined and static parameters (t hresholds, number of clusters and learning rates) found in current algorithms st ill limit the application of clustering to data streams. This limitation motivated this esis, which proposes a continuous approach to evaluate behavior variations and ad apt algorithm inductive bias by changing its parameters. The main contribution of th is esis is the proposal of three approaches to adapt induction bias: i) an a ppro ch based on the design of an adaptive artificial self-organizing neural network architecture that enables behavior evaluation by means of Markov chain an d Shannon entropy estimations; ii) an approach to adapt traditional data clus tering algorithms according to behavior variations in sequences of data chunks; and iii) an approach based on the proposed neural network architecture to continuousl y adapt parameters by means of the evaluation of data stability. Additionally, in order to analyze the essential characteristics of data streams, this thesis pre sents a formalization for the problem of data stream clustering and a taxonomy on approach es to detect behavior variations.

Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados