Identification of adverse disease agents and risk analysis using frequent pattern mining

Life-threatening illnesses such as cancer, cirrhosis of the liver, and hepatitis have become crucial problems for humanity. The risk of mortality can be deflated by early detection of symptoms and providing the best possible diagnosis. This critical role of detection and/or diagnosis can be enhanced using one of the techniques used in data mining, such as periodic pattern mining, association rule mining, classification. Analyzing the commonly occurring possible patterns or signs followed by performing the correlation analysis among those patterns can be exhaustively practiced for early detection and improve the diagnosis. Towards the adoption of association rule mining, devising a cost-effective and time-saving algorithm for mining frequent patterns plays an important role. In this paper, we propose an approach to pattern mining called Improved Frequent Pattern Growth (Improved FP Growth). Firstly, it constructs an improvised frequent pattern tree data structure called Improved FP-tree. Moreover, Improved FP-Growth introduces a construction of conditional FP-tree data structure layout called Improved Conditional Frequent Pattern Tree (Improved Conditional FP-Tree). Unlike the traditional FP-Growth method, it uses both top-down and bottom-up approaches to efficiently generate frequent patterns without recursively constructing the improved conditional FP-tree. The experimental results emphasize the significance of the proposed Improved FP-Growth algorithm over a few traditional frequent itemset mining algorithms those adopt the approach of recursive conditional FP-tree construction. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces an approach to pattern mining called Improved Frequent Pattern Growth, which constructs an Improved FP-tree data structure and introduces a layout of Conditional FP-tree for efficient generation of frequent patterns. The experimental results highlight the significance of the proposed Improved FP-Growth algorithm over traditional frequent itemset mining algorithms.