Missing value replacement in strings and applications

Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for pr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Data mining and knowledge discovery Jg. 39; H. 2; S. 12
Hauptverfasser: Bernardini, Giulia, Liu, Chang, Loukides, Grigorios, Marchetti-Spaccamela, Alberto, Pissis, Solon P., Stougie, Leen, Sweering, Michelle
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Springer Nature B.V 01.03.2025
Springer US
Schlagworte:
Algorithms
Clustering
Combinatorial analysis
Datasets
Graphical representations
Modelling
Optimization
Pattern matching
Privacy
Sequences
Shortest path algorithms
Shortest-path problems
Strings
String algorithms
Missing value replacement
String sanitization
Forbidden patterns
ISSN:1384-5810, 1573-756X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for privacy protection. In order to analyze such datasets, it is often important to replace each missing value, with one or more valid letters, in an efficient and effective way. Here we formalize this task as a combinatorial optimization problem: the set of constraints includes the context of the missing value (i.e., its vicinity) as well as a finite set of user-defined forbidden patterns, modeling, for instance, implausible or confidential patterns; and the objective function seeks to minimize the number of new letters we introduce. Algorithmically, our problem translates to finding shortest paths in special graphs that contain forbidden edges representing the forbidden patterns. Our work makes the following contributions: (1) we design a linear-time algorithm to solve this problem for strings over constant-sized alphabets; (2) we show how our algorithm can be effortlessly applied to fully sanitize a private string in the presence of a set of fixed-length forbidden patterns [Bernardini et al. 2021a]; (3) we propose a methodology for sanitizing and clustering a collection of private strings that utilizes our algorithm and an effective and efficiently computable distance measure; and (4) we present extensive experimental results showing that our methodology can efficiently sanitize a collection of private strings while preserving clustering quality, outperforming the state of the art and baselines. To arrive at our theoretical results, we employ techniques from formal languages and combinatorial pattern matching.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-024-01074-3