An innovative temporal data mining

Abstract

Data Mining is the extraction on meaningful information from stored data, useful for good decision making. The management of such useful information is called knowledge management. The Principle of Data Mining is better to use complicative primitive patterns and simple logical combination than simple primitive patterns and complex logical form. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance from the user perspective. Priority based temporal mining (PBTM) is found to have a better performance than the time based A priori when compared the terms of the execution time and computation. The new algorithm for minimum weighted fuzzy mining involves performing temporal rule mining and presenting the rules which change with time for the time interval under consideration. The presentation of the rules change with time, involves dividing the itemset space into partitions for identifying the changing rules easily. The temporal database consists of items which are prioritized by assigning weightage. The fuzzy approach is applied by assigning the triangular membership functions to the weighted items which gives better results than quantitative values. The experimental results are obtained from the complaints database of the telecommunications systems, which shows the most feasible method for temporal mining. The application of Ant Colony systems as a classification rule discovery is explored and probably to perform a flexible search over all possible logic combinations of the predicting attributes. The fuzzy rules are used in a temporal data mining system, rather than classification rules in the sense of data mining. newline newline newline