developing and improved the intelligent search of sequence of patterns using fuzzy ontology and semantic similarities approach

Classify and pattern selection in a set of data by applying pattern searchalgorithms or sequencing algorithm is called sequence mining .the purpose ofidentifying these patterns in data collection is analysis and interpretation ofthe data that are extracted from an event . in other words, we are looking toidentify more detailed of systems that analysis of their events is desired.however, due to the complex and ambiguous nature of these systems,interpretations of the dependant events are always difficult. depending on theevents that their data should be analysis and interpret and also the type ofextracted date structure, several algorithms have been proposed to findpatterns. a criterion to classify and patterns selection is counting and numberof repetitions of a particular data item in a sequence of data. such an approachwill be attention the schematic similarities and by selecting a sequence withthis approach, perfect analysis and interpretation of systems event will not beexpected. ontology is clear and formal definition of a concept for theknowledge presentation that is understandable for humans and machine. so,the possibility of sharing it between different applications is provided. weproposed an intelligent search of sequence using fuzzy ontology and semanticsimilarity algorithm for looking at the similarity between items for identifymore general patterns of sequence and number of repeats. get sequences withsuch properties could facilitate the process of analysis and interpretation ofcase study events.