NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA

c © All rights reserved. This work may not be reproduced in whole or in part, by photo-copy or other means, without the permission of the author. ii Data mining is the process of extracting knowledge from large amounts of data. Itcovers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applica-tions that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the asso-ciation rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an ef-ficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types o