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 liner 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.
Framework temporal data first-order temporal logic rule evaluation
DING PAN YAN PAN
Management School, Jinan University, Guangzhou 510632, China;Department of Computer Science and Tech School of Management, Fuzhou University, Fuzhou 350002, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1019-1024
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）