AN EFFICIENT APPROACH FOR MINING UNCERTAIN FREQUENT PATTERNS USING DYNAMIC DATA STRUCTURE WITHOUT FALSE POSITIVES

ABSTRACT Traditional frequent pattern mining focuses on databases with exact information. The concept of uncertain pattern mining was recently proposed to fulfill the demand for processing databases with uncertain data, and various relevant methods have been devised.State-of-the-art methods based on tree structure can cause mortal problems in terms of runtime and memory usage according to the characteristics of uncertain databases and threshold settings because their own tree data structures can become excessively large and complicated in their mining processes. And also it cannot apply importance of each item obtained from the real world into the mining process. To overcome such problems various approximation approaches have been suggested. So that propose an exact, efficient algorithm for uncertain frequent pattern mining based on novel dynamic data structures and mining techniques, which can also guarantee the correctness of the mining results without any false positives. The newly proposed linked list based data structure and mining techniques allow a complete set of uncertain frequent patterns to be mined more efficiently. KEYWORDS:Data mining, Existential probability, Uncertain pattern, Data structure, Correctness.