Behavior studies have been conducted by scientists and philosophers who approach subjects such as star and planet trajectories, society organizations, living beings evolution and human language. With the advent of computer, new challenges have been observed in order to explore and understand the behavior variations of interactions with systems. Motivated by those challenges, this work proposes a new approach to automatically cluster, detect and identify behavior patterns. In order to validate this approach, we have modeled the knowledge embedded in interactions of handwriting signatures. The generated knowledge models were, afterwards, employed to verify signatures. Obtained results were compared to other related approaches presented in SVC2004, the First International Signature Verification Competition.

The multimedia document generator, iClass system, has been used by professors from the Institutes of Chemistry, Mathematics and Computer Science from the University of São Paulo aiming at helping the multimedia content production and availability. Data from user interactions, available in iClass system, have motivated this work which aims at studying the user behavior under different circumstances. The behavior extracted makes possible the analysis of different patterns of the same user, among groups, and distinct users. Those pattern differences should help to understand user evolution in iClass system under diverse situations. The data are grouped by a neural network and afterwards Markov Chains are built to represent their behaviors in different time moments. The detected user or group behavior variations are related to classify profiles and comprehend them in different situations.

Agradeço primeiramente a Deus, em seguida a meus pais pela oportunidade de realizar meus estudos. Em especialà Alessandra Kelli Barbato pelo carinho, incentivo e compreenssão. Aos meus amigos que diretamente auxiliaram no desenvolvimento das pesquisas apresentadas nesta dissertação: Evgueni Dodonov, José Augusto Andrade Filho e Marcelo Keese Albertini. A paciência, confiança e dedicação de meu amigo e orientador Rodrigo Fernandes de Mello. A CAPES e FAPESP pelo apoio dado a este trabalho. Aos amigos de faculdade e demais pessoas que auxiliaram direta ou indiretamente nesta dissertação.