Discretizing Numerical Attributes: An Analysis of Human Perceptions
Minakshi Kaushik Rahul Sharma Dirk Draheim
Machine learning (ML) has employed various discretization methods to
partition numerical attributes into intervals. However, an effective
discretization technique remains elusive in many ML applications, such as
association rule mining. Moreover, the existing discretization techniques do
not reflect best the impact of the independent numerical factor on the
dependent numerical target factor. This research aims to establish a benchmark
approach for numerical attribute partitioning. We conduct an extensive analysis
of human perceptions of partitioning a numerical attribute and compare these
perceptions with the results obtained from our two proposed measures. We also
examine the perceptions of experts in data science, statistics, and engineering
by employing numerical data visualization techniques. The analysis of collected
responses reveals that $68.7\%$ of human responses approximately closely align
with the values generated by our proposed measures. Based on these findings,
our proposed measures may be used as one of the methods for discretizing the
numerical attributes.