Abstract
FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.
Highlights
Data mining is a process of obtaining potentially useful knowledge from data[1]
As an important part of data mining, association rule mining reflects the intrinsic relationship between complex itemsets [2]
This paper proposes an improved FP-Growth algorithm based on adjacency table which draws on the idea of graphs
Summary
Data mining is a process of obtaining potentially useful knowledge from data[1]. As an important part of data mining, association rule mining reflects the intrinsic relationship between complex itemsets [2]. Han et al proposed the FP-Growth algorithm using the FP-Tree to generate frequent itemsets [5]. This paper proposes an improved FP-Growth algorithm based on adjacency table which draws on the idea of graphs. The algorithm makes full use of the established adjacency table, and only needs to scan the original transaction database once. It has the advantages of fast running speed, small memory consumption and low complexity. The rest of this paper is organized as follows: In Section 2, related works are discussed.The section 3, we proposes the improvement of the FP-Growth algorithm based on adjacent table and the mining process of frequent itemsets. We present our conclusions and future work
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