Discovery of interesting frequent item sets in an uncertain database using ant colony optimization

ABSTRACT

Applications like business basket analysis, digital service analytics, bio-informatics, and mobile commerce have greatly benefited from the information retrieval of significant features from massive databases for improved decision-making. Item set mining is used to find intriguing patterns in databases. Discovering item sets in an uncertain database is a tedious task. Only mathematical correlations between the elements in an item set are the exclusive subject of recurring item set mining research. The finding is direct to optimal. This article introduces an ant colony that maps the viable solution space to a directed graph with quadratic space complexity. The proposed model evaluates an uncertain transaction database's item set. Compared to the current methods, the findings demonstrate the importance of the proposed model.

KEYWORDS:

• Patterns
• association rule mining
• frequent items
• database

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.