Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the userspecified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Spatial Weighted Frequent Itemset (SWFI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many realworld applications. For instance, an SWFI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated SWFIs do not satisfy the antimonotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A patterngrowth algorithm, called Spatial Weighted Frequent Patterngrowth, has also been presented to find all SWFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.

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.