Approximate Maximal Frequent Pattern Mining With Weight Conditions And Error Tolerance

Since the concept of frequent pattern mining was proposed, there have been many efforts to obtain useful pattern information from large databases. As one of them, applying weight conditions allows us to mine weighted frequent patterns considering unique importance of each item composing databases, and the result of analysis for the patterns provides more useful information than that of considering only frequency or support information. However, although this approach gives us more meaningful pattern information, the number of patterns found from large databases is extremely large in general; therefore, analyzing all of them may become inefficient and hard work. Thus, it is essential to apply a method that can selectively extract representative patterns from the enormous ones. Moreover, in the real-world applications, unexpected errors such as noise may occur, which can have a negative effect on the values of databases. Although the changes by the error are quite small, the characteristics of generated patterns can be turned definitely. For this reason, we propose a novel algorithm that can solve the above problems, called AWMax (an algorithm for mining Approximate weighted maximal frequent patterns (AWMFPs) considering error tolerance). Through the algorithm, we can obtain useful AWMFPs regardless of noise because of the consideration of error tolerance. Comprehensive performance experiments present that the proposed algorithm has more outstanding performance than previous state-of-the-art ones.