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Related Experiment Video

Updated: May 30, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Missing value replacement in strings and applications.

Giulia Bernardini¹, Chang Liu², Grigorios Loukides³

¹Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy.

Data Mining and Knowledge Discovery

|January 27, 2025

Summary

This summary is machine-generated.

This study introduces a new algorithm to efficiently replace missing values in sequential data, minimizing introduced letters while respecting context and forbidden patterns. The method effectively sanitizes private strings and preserves clustering quality.

Keywords:

Forbidden patterns Missing value replacement String algorithms String sanitization

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Area of Science:

Computer Science
Bioinformatics
Data Science

Background:

Missing values are common in sequential data due to measurement errors, flexible modeling, or privacy concerns.
Analyzing such data requires efficient and effective methods for replacing missing values with valid characters.
Existing methods may not adequately address constraints like context and forbidden patterns.

Purpose of the Study:

To formalize the problem of replacing missing values in sequential data as a combinatorial optimization problem.
To develop an efficient algorithm for solving this problem, considering context and forbidden patterns.
To apply the algorithm for sanitizing private strings and clustering collections of strings.

Main Methods:

Formalizing the problem as finding shortest paths in graphs with forbidden edges.
Designing a linear-time algorithm for strings over constant-sized alphabets.
Applying techniques from formal languages and combinatorial pattern matching.

Main Results:

A linear-time algorithm for efficient missing value replacement in sequential data.
Demonstration of the algorithm's effectiveness in fully sanitizing private strings.
A methodology for sanitizing and clustering private string collections that preserves clustering quality.
Experimental results showing superior performance over state-of-the-art methods.

Conclusions:

The proposed algorithm efficiently handles missing values in sequential data under complex constraints.
The methodology offers effective privacy protection for string datasets while maintaining data utility for clustering.
This work advances the state of the art in data sanitization and analysis of sequential data.