21,449 research outputs found
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
Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
Unsupervised image segmentation algorithms aim at identifying disjoint
homogeneous regions in an image, and have been subject to considerable
attention in the machine vision community. In this paper, a popular theoretical
model with it's origins in statistical physics and social dynamics, known as
the Deffuant-Weisbuch model, is applied to the image segmentation problem. The
Deffuant-Weisbuch model has been found to be useful in modelling the evolution
of a closed system of interacting agents characterised by their opinions or
beliefs, leading to the formation of clusters of agents who share a similar
opinion or belief at steady state. In the context of image segmentation, this
paper considers a pixel as an agent and it's colour property as it's opinion,
with opinion updates as per the Deffuant-Weisbuch model. Apart from applying
the basic model to image segmentation, this paper incorporates adjacency and
neighbourhood information in the model, which factors in the local similarity
and smoothness properties of images. Convergence is reached when the number of
unique pixel opinions, i.e., the number of colour centres, matches the
pre-specified number of clusters. Experiments are performed on a set of images
from the Berkeley Image Segmentation Dataset and the results are analysed both
qualitatively and quantitatively, which indicate that this simple and intuitive
method is promising for image segmentation. To the best of the knowledge of the
author, this is the first work where a theoretical model from statistical
physics and social dynamics has been successfully applied to image processing.Comment: This paper is under consideration at Signal Image and Video
Processing journa
Dynamic Fuzzy c-Means (dFCM) Clustering and its Application to Calorimetric Data Reconstruction in High Energy Physics
In high energy physics experiments, calorimetric data reconstruction requires
a suitable clustering technique in order to obtain accurate information about
the shower characteristics such as position of the shower and energy
deposition. Fuzzy clustering techniques have high potential in this regard, as
they assign data points to more than one cluster,thereby acting as a tool to
distinguish between overlapping clusters. Fuzzy c-means (FCM) is one such
clustering technique that can be applied to calorimetric data reconstruction.
However, it has a drawback: it cannot easily identify and distinguish clusters
that are not uniformly spread. A version of the FCM algorithm called dynamic
fuzzy c-means (dFCM) allows clusters to be generated and eliminated as
required, with the ability to resolve non-uniformly distributed clusters. Both
the FCM and dFCM algorithms have been studied and successfully applied to
simulated data of a sampling tungsten-silicon calorimeter. It is seen that the
FCM technique works reasonably well, and at the same time, the use of the dFCM
technique improves the performance.Comment: 15 pages, 10 figures. It is accepted for publication in NIM
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