PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets

Abstract

The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. The syntax is simple, and the tools show the exploration progress in real-time. In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment. In addition, any user can perform further selections based on the results printed in the console. The paper contains examples of using these tools for large datasets such as the one belonging to the World Values Survey and based on a simple variable naming practice. This article includes many recorded simulations and presents performance results. They depend on different resources and hardware configurations used, including cloud vs. on-premises, large vs. small amounts of RAM and processing cores, and in-memory vs. traditional storage.

Exploring the Predictors of Co-Nationals’ Preference over Immigrants in Accessing Jobs—Evidence from World Values Survey

“…After performing the second phase meant for filters starting from pairwise correlation coefficients as absolute values (≥0.1), together with their significance (p < 0.001) and support (at least a third of the data or N > 142,150), 19 variables resulted as indicated in Table 1. The same results were more easily achieved using the PCDM command (Stata script at https://tinyurl.com/25pd6mx6, accessed on 30 January 2023) in Stata [73] and three parameters (minacc (0.1) minn (142,150) maxp (0.001)) corresponding to those three filters above. The next concern before going to the third selection step (dedicated to cross-validations on specified criteria) was to recode ("nt" call sign meaning null treatment) the remaining variables (all 19 in Table 1).…”

“…The 2nd selection round stood on a set of filters applied. First, they met a minimum threshold of 0.1 [72] for the absolute values of pairwise correlation coefficients [73] between each recoded variable from the previous step and the one that was to be analyzed. In addition, there was a minimum value of the corresponding significance (min p = 0.001) and a minimum support afferent to a minimum number of valid observations (at least a third of the total number) for each pair.…”

The Impact of Corporate Governance on the Financial Performance of Listed Companies: Evidence from Saudi Arabia

“…Multicollinearity is considered a phenomenon that supports the model of regression That is utilised through the support of the independent variable. However, all these factors help to generate the accurate outcome of the research (Homocianu & Airinei, 2022). The concerning factor is mainly pointed out by the values that lie between -1 and 1 and r. All these factors initially display the negative interrelationship then it shows the positive interrelationship that can be denoted by different numerical factors.…”