504 research outputs found
Fuzzy clustering of spatial interval-valued data
In this paper, two fuzzy clustering methods for spatial intervalvalued
data are proposed, i.e. the fuzzy C-Medoids clustering
of spatial interval-valued data with and without entropy regularization.
Both methods are based on the Partitioning Around
Medoids (PAM) algorithm, inheriting the great advantage of
obtaining non-fictitious representative units for each cluster.
In both methods, the units are endowed with a relation
of contiguity, represented by a symmetric binary matrix. This
can be intended both as contiguity in a physical space and as
a more abstract notion of contiguity. The performances of the
methods are proved by simulation, testing the methods with
different contiguity matrices associated to natural clusters of
units. In order to show the effectiveness of the methods in
empirical studies, three applications are presented: the clustering
of municipalities based on interval-valued pollutants levels, the
clustering of European fact-checkers based on interval-valued
data on the average number of impressions received by their
tweets and the clustering of the residential zones of the city of
Rome based on the interval of price values
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems
- …