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Updated: May 1, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Hyper-structure mining of frequent patterns in uncertain data streams.

Chandima Hewanadungodage¹, Yuni Xia¹, Jaehwan John Lee²

¹Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA.

Knowledge and Information Systems

|April 15, 2014

Summary

This summary is machine-generated.

This study introduces novel hyper-structure-based algorithms for efficiently mining frequent itemsets in uncertain data streams. These new methods, UHS-Stream and TFUHS-Stream, outperform existing tree-based approaches in accuracy and efficiency.

Keywords:

Data mining Data stream Data uncertainty Frequent patterns

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Area of Science:

Data Mining
Stream Data Analysis
Uncertainty Quantification

Background:

Real-world applications like sensor monitoring and medical diagnostics generate uncertain data streams.
Existing frequent pattern mining methods struggle with data uncertainty and stream processing.

Purpose of the Study:

To develop efficient algorithms for discovering frequent itemsets in uncertain data streams.
To address the limitations of current FP-tree-based approaches in handling data uncertainty.

Main Methods:

Proposed two novel hyper-structure-based algorithms: UHS-Stream and TFUHS-Stream.
UHS-Stream finds all frequent itemsets up to the current time.
TFUHS-Stream incorporates a time-fading approach for dynamic stream analysis.

Main Results:

The proposed hyper-structure-based algorithms significantly outperform existing tree-based methods.
Demonstrated improvements in accuracy, runtime, and memory usage.
Effective handling of data uncertainty in continuous data streams.

Conclusions:

Hyper-structure-based approaches offer a superior solution for frequent pattern mining in uncertain data streams.
UHS-Stream and TFUHS-Stream provide efficient and accurate methods for real-time data analysis.
The developed algorithms are suitable for applications with inherent data uncertainty and high data velocity.