A General Framework on Temporal Data Mining

Mass processing request has made temporal data mining a vital branch of data mining field. A general framework for temporal knowledge discovery is proposed to define primary concepts in first-order linear temporal logic. The sequence is transformed firstly into linear ordered sequence of events consisted of basic strings. The framework represents a rule in quasi-Horn clause, defines the measures of the first-order formula valuating on a linear state structure, generates the estimator sequence of the measures based on a session model, quantifies the novelty of the discovered rules in terms of deviations among the rules using dynamic time warping distance function, and proves the relevant properties of the concepts. A process model of continuous data mining is developed, based on the session model