Temporal analysis of political instability through descriptive subgroup discovery
Abstract: This paper analyzes the Political Instability Task Force (PITF) data set using a new methodology based on machine learning tools for subgroup discovery. While the PITF used static data, this study employs both static and dynamic descriptors covering the 5-year period before onset. The methodology provides several descriptive models of countries especially prone to political instability. For the most part, these models corroborate the PITF’s findings and support earlier theoretical works. The paper also shows the value of subgroup discovery as a tool for developing a unified concept of political instability as well as for similar research designs
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Veröffentlichungsversion
begutachtet (peer reviewed)
In: Conflict Management and Peace Science ; 25 (2008) 1 ; 19-32
- Klassifikation
-
Politik
- DOI
-
10.1080/07388940701860359
- URN
-
urn:nbn:de:0168-ssoar-368876
- Rechteinformation
-
Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Lambach, Daniel
- Gamberger, Dragan
Entstanden
- 2008
Ähnliche Objekte (12)
Temporal analysis of political instability through descriptive subgroup discovery
Subgroup discovery for structured target concepts
Novel techniques for efficient and effective subgroup discovery
Novel Techniques for Efficient and Effective Subgroup Discovery
Leveraging the potential of multi-layer networks for subgroup discovery
Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery
Subgroup growth
Data Mining Methods for Medical Diagnosis : Test Selection, Subgroup Discovery, and Contrained Clustering
Data mining methods for medical diagnosis : test selection, subgroup discovery, and constrained clustering
Subgroup lattices of groups
Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm
Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery
Temporal analysis of political instability through descriptive subgroup discovery
Subgroup discovery for structured target concepts
Novel techniques for efficient and effective subgroup discovery
Novel Techniques for Efficient and Effective Subgroup Discovery
Leveraging the potential of multi-layer networks for subgroup discovery
Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery
Subgroup growth
Data Mining Methods for Medical Diagnosis : Test Selection, Subgroup Discovery, and Contrained Clustering
Data mining methods for medical diagnosis : test selection, subgroup discovery, and constrained clustering
Subgroup lattices of groups
Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm
Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery
Temporal analysis of political instability through descriptive subgroup discovery
Subgroup discovery for structured target concepts
Novel techniques for efficient and effective subgroup discovery
Novel Techniques for Efficient and Effective Subgroup Discovery
Leveraging the potential of multi-layer networks for subgroup discovery
Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery
Subgroup growth
Data Mining Methods for Medical Diagnosis : Test Selection, Subgroup Discovery, and Contrained Clustering
Data mining methods for medical diagnosis : test selection, subgroup discovery, and constrained clustering
Subgroup lattices of groups
Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm