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O objetivo desta dissertação foi estudar um conjunto de empresas cotadas na bolsa de
valores de Lisboa, para identificar aquelas que têm um comportamento semelhante ao
longo do tempo. Para isso utilizamos algoritmos de Clustering tais como K-Means,
PAM, Modelos hierárquicos, Funny e C-Means tanto com a distância euclidiana como
com a distância de Manhattan. Para selecionar o melhor número de clusters identificado
por cada um dos algoritmos testados, recorremos a alguns índices de
avaliação/validação de clusters como o Davies Bouldin e Calinski-Harabasz entre outros.
The aim of this thesis was to study a set of companies from Lisbon stock exchange to identify those that have a similar behavior over time. For this we use clustering algorithms such as K-Means, PAM, hierarchical models, Funny and C-Means with Euclidean distance and Manhattan distance. To select the best number of clusters identified by each of the tested algorithms, we resort to some clusters validation such as the Davies Bouldin and Calinski-Harabasz among others.
The aim of this thesis was to study a set of companies from Lisbon stock exchange to identify those that have a similar behavior over time. For this we use clustering algorithms such as K-Means, PAM, hierarchical models, Funny and C-Means with Euclidean distance and Manhattan distance. To select the best number of clusters identified by each of the tested algorithms, we resort to some clusters validation such as the Davies Bouldin and Calinski-Harabasz among others.