An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Abstract

Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

Research on improved algorithm of FP-growth based on data strong association

“…The support is set within 0.05-0.2, In this paper, FP-Growth algorithm based on strong data association, Hussein A. Alsaeedi et at. [12] proposed a comparison of FP-growth algorithm based on interestingness, IOC-FP-growth method [13] and traditional FP-Growth algorithm are used for comparison, as shown in Figure 4: It can be seen that as the support increases, the advantage of the SDA-FP-Growth algorithm over a comparison of FPgrowth algorithm based on interestingness and the traditional algorithm gradually decreases. When the support is 0.05 times, the running time of the FP-Growth algorithm based on strong data association is 69.6% less than that of the traditional FP-Growth algorithm, 24% less than that of the IOC-FP-growth method, and 12% less than that of the FP-Growth algorithm based on interest degree.…”

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

“…It is thus more rapid than Apriori. The FP-growth approach has numerous alternatives and extensions [20], [28], [29], [30], and [31]. Some of the alternatives and extensions of the FP-growth approach include Eclat [20], FPGrowthPlus [21], FPMax [22], and FPGrowthHUI [23].…”