Effective number of parties for incomplete data

Image Reconstruction from Incomplete Data

Constructing the Number of Parties

Forming an idea of the number of parties competing at elections or winning seats in legislatures is fundamental to disaggregated approaches to mapping party systems. We set out a method for systematically relating the behaviour of any ‘number of parties’ index to the size of the largest party’s vote and the numbers of parties in competition. This approach shows that the ‘effective number of par...

Image Reconstruction from Incomplete Data

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

Rethinking Duverger’s Law: Predicting the Effective Number of Parties in Plurality and PR Systems

Attempts to predict the number of political parties emerging in democracies have usually been based on one of two seemingly incompatible approaches: ( 1 ) the ‘institutional’ approach (e.g.. Duverger’s Law and Hypothesis) focuses o n the nature of the electoral system and also on the number of seats per district: (2) the ‘ideological’ approach stresses the nature and extent of social cleavages....