Abstract
Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression Frequent Pattern Tree (FCFP-Tree) and related algorithms called FCFPIM. Unlike FP-tree, the FCFP-Tree maintains complete information of all the frequent and infrequent items in the original dataset. This allows the FCFPIM algorithm not to waste any scan and computational overhead for the previously processed original dataset when new dataset are added and support changes. Therefore, much processing time is saved. Importantly, FCFPIM adopts an effective tree structure adjustment strategy when the support of some items changes due to the arrival of new data. FCFPIM is conducive to speeding up the performance of incremental FIM. Although the tree structure containing the lossless items information is space-consuming, a compression strategy is used to save space. We conducted experiments to evaluate our solution, and the experimental results show the space-consuming is worthwhile to win the gain of execution efficiency, especially when the support threshold is low.
Highlights
INTRODUCTION FRequent itemsets mining (FIM) has become an important research aspect of data mining and plays an fundamental role in association rule mining [1], [2], sequence mining [3], [4], correlations mining [5], [6]and the like [7]–[10]
When new data is applied, in order to avoid scanning the original dataset during incremental mining, we extend the FP-tree structure to involve infrequent items information
WORK In this paper, for most incremental mining algorithms, it is necessary to repeatedly scan the original datasets or the tree structure adjustment cost is too high, which leads to the low efficiency of incremental mining
Summary
INTRODUCTION FRequent itemsets mining (FIM) has become an important research aspect of data mining and plays an fundamental role in association rule mining [1], [2], sequence mining [3], [4], correlations mining [5], [6]and the like [7]–[10]. When new data are added, incremental mining algorithms need to process them to maintain the tree structure. To avoid the overhead of re-scanning and reconstructing the tree corresponding to the original dataset when an item changes from infrequent to frequent in the incremental mining process.
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