Work

Overview of Interestingness Measures for Data Mining

Year: 2025

Type: article

Abstract: In data mining, the primary goal is to discover hidden patterns and knowledge from large datasets. However, not all patterns discovered during the mining process are useful or relevant. To enhance the efficacy of data mining, interestingness measures are developed to evaluate the quality and significance of these patterns. This paper explores the concept of interestingness measures in data mining, categorizes existing approaches, and proposes new metrics for improving the relevance and usefulness of discovered patterns. We also discuss the implications of these measures for real-world applications in various domains, such as business, healthcare, and social sciences.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15533027

Author: Dr. Swati Joshi

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6931085294

FWCI:

Cites:

Cited by:

Related to: