Mining and Classification of Multivariate Sequential Data

Multivariate sequence mining and classification are important and challenging tasks. They can be applied to numerous domains including medical diagnosis, handwriting deficiency diagnosis, identification of users for security or personalized TV services, and even transportation and traffic planning. The problem we address in this dissertation is classification of multivariate sequences. Multivariate sequences are sequences that have multiple attributes for each item in the sequence. Several attempts to address this problem exist, but none provide a full solution. One type of solution to this problem is to reduce the solution to a single attribute or non sequential problem while loosing valuable information. Other solutions address both the multivariate and the sequential aspect of the input but provide an unscalable solution. In this dissertation we first present COACH (Cumulative Online Algorithm for Classification of Handwriting deficiencies). COACH is a classification algorithm for multivariate sequences that uses heuristics to combine several single attribute classifications. COACH is evaluated on real data obtained from children with poor handwriting using a digitizer tablet. Results show that COACH manages to successfully differentiate between poor to proficient handwriting. Integrating several single attribute classifications encouraged us to search for a solution that uses all the attributes together in the classification process. The second part of the dissertation introduces frequent sequence mining. Frequent sequence mining, as well as being a challenging and interesting task, can be used for