Exploiting Data Mining Techniques for Compressing Table Constraints In this paper, we propose an improvement of the compression step of sliced table method proposed by Gharbi et al. 1 for compressing and solving table constraints. We consider only nary CSP defined in extensional form. More precisely, we propose to use the cover of an itemset in the FPtree instead of its frequency to improve the construction step of the resulting compressed tables. Moreover, we propose to exploit the compression rate metric instead of savings to compute frequent itemsets relevant for compression. This allows higher compression and leads to an efficient resolution of compressed tables by identifying more accurate frequent itemsets necessary for compression. Experimental results show the effectiveness and efficiency of our approach.

On a Parallel Spark Workflow for Frequent Itemset Mining Based on Array PrefixTree Frequent Itemset Mining (FIM) is a fundamental procedure in various data mining techniques such as association rule mining. Among many existing algorithms, FPGrowth is considered as a milestone achievement that discovers frequenti temsets without generating candidates. However, due to the high complexity of its mining process and the high cost of its memory usage, FPGrowth still suffers from a performance bottleneck when dealing with large datasets. In this paper, we design a new Array PrefixTree structure, and based on that, propose an Array PrefixTree Growth (APTGrowth) algorithm, which explicitly obviates the need of recursively constructing conditional FPTree as required by FPGrowth. To support big data analytics, we further design and implement a parallel version of APTGrowth, referred to as PAPTGrowth, as a Spark workflow. We conduct FIM workflow experiments on both reallife and synthetic datasets for performance evaluation, and extensive results show that PAPTGrowth outperforms other representative parallel FIM algorithms in terms of execution time, which sheds light on its potential applications to big data mining.