The Automated Field Correlation approach is a data analysis technique used in exploratory data analysis and digital forensics. It involves systematically applying statistical methods, such as calculating Pearson or Spearman correlation coefficients, to every pair of fields (variables) within a dataset. This process automatically identifies the strength and direction (positive or negative) of linear or monotonic relationships between variables. The goal is to uncover significant patterns and dependencies across single or multiple fields without prior hypotheses, which directly matches the method described in the question.

A. Graph-based approach: This method models relationships as nodes and edges, focusing on network structure, paths, and connectivity rather than systematically calculating statistical correlation coefficients between all data fields.

B. Neural network-based approach: This approach learns complex, often non-linear relationships implicitly through its layered structure and weights during training. It does not perform an explicit, systematic check of pairwise correlations.

C. Rule-based approach: This approach applies a set of predefined "if-then" rules to data. It is used for classification or decision-making based on known conditions, not for discovering unknown statistical correlations.

1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer. In Chapter 3, Section 3.3.3, the concept of the correlation matrix is discussed as a tool to systematically examine the pairwise correlations between all variables in a dataset. This forms the basis of an automated field correlation approach. (Available via Stanford University: https://web.stanford.edu/~hastie/ISLRv2website.pdf)

2. Guttag, J. V. (2016). 6.0002 Introduction to Computational Thinking and Data Science. MIT OpenCourseWare. Lecture 9, "Statistical Thinking," covers the computation of correlations between variables in a dataset as a fundamental step in data analysis. The accompanying code demonstrates the automation of this process to systematically compare fields. (See Lecture 9 notes and code examples).

3. Beebe, N. L. (2009). Digital Forensic Triage: An Application of Automated Forensic Hypothesis Generation and Testing. University of Texas at San Antonio, Department of Information Systems and Technology Management. This dissertation discusses the automation of forensic analysis, including "automated correlation" (Chapter 4, Section 4.2.2) to systematically identify relationships between different data artifacts, aligning with the principles of the automated field correlation approach.