Abstract
A growing amount of data is being generated, communicated and processed at the edge nodes of cloud systems; this has the potential to improve response times and thus reduce communication bandwidth. We found that traditional static association rule mining cannot solve certain real-world problems with dynamically changing data. Incremental association rule mining algorithms have been studied. This paper combines the fast update pruning (FUP) algorithm with a compressed Boolean matrix and proposes a new incremental association rule mining algorithm, named the FUP algorithm based on a compression matrix (FBCM). This algorithm requires only a single scan of both the database and incremental databases, establishes two compressible Boolean matrices, and applies association rule mining to those matrices. The FBCM algorithm effectively improves the computational efficiency of incremental association rule mining and hence is suitable for knowledge discovery in the edge nodes of cloud systems.
Highlights
Since Nature published its special issue on big data in 2008 [1], big data research has rapidly developed and become a hot research topic that has attracted substantial attention [2]–[4]
Each iteration requires the entire transaction database to be scanned; the search space of the solution is very large. To solve these two problems, this paper combines the fast update pruning (FUP) algorithm with the idea of a compressed Boolean matrix and proposes the algorithm FUP based on a compression matrix (FBCM), which is a new, improved algorithm
Considering the computing capacity and network bandwidth constraints of edge nodes, this paper proposes an incremental association rule mining algorithm based on matrix compression
Summary
Since Nature published its special issue on big data in 2008 [1], big data research has rapidly developed and become a hot research topic that has attracted substantial attention [2]–[4]. The incremental association rule mining that is considered in this paper mainly relates to the case in which the data volume increases. The FUP algorithm is a substantial improvement compared with traditional static association rule mining algorithms. The FUP algorithm prunes existing frequent items It receives new frequent itemsets from incremental databases. Each iteration requires the entire transaction database to be scanned; the search space of the solution is very large To solve these two problems, this paper combines the FUP algorithm with the idea of a compressed Boolean matrix and proposes the algorithm FUP based on a compression matrix (FBCM), which is a new, improved algorithm. The FBCM algorithm compresses and prunes the transaction matrix in each iteration to further reduce the storage space and the number of computations. The fifth section presents experimental details for comparison of the FBCM algorithm with the classical FUP algorithm to demonstrate the advantages of the FBCM algorithm
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