Mining Closed Sequential Patterns Using Condensed WAP Tree

Abstract

quotGeneration of data with an inherent sequential nature is the order of today s digital society. This newlinekind of data could be composed of discrete events that have either a temporal or spatial ordering newlineand is generally obtained by sectors like telecommunication networks, E-Commerce , Internet newlineservers and gene databases, medical domain to name a few. The ability to explore and exploit newlinethe sequential nature of the data for prediction leverages strategic decision making and problem newlinesolving. Symbolic sequence data consists of long sequence of ordered events with possible newlinerelationships among them. Symbolic sequence mining techniques aim at extracting frequent newlinesequential patterns for huge collections of event sequences based on the user defined minimum newlinesupport threshold. From a give set of symbols / events due to the possible repetition of events newlineinfinitely large number of sequences is possible and hence the task of extracting frequent newlinesequences is much more complex compared to extracting frequent itemsets. Analogous to closed newlineitemsets the set of sequential patterns automatically eliminates a lot of redundancy from the set newlineof all frequent sequences and provides a concise set of patterns maintaining completeness.quot newline newline