Search results for "Cluster Analysis"
showing 10 items of 848 documents
Distributed Data Clustering via Opinion Dynamics
2015
We provide a distributed method to partition a large set of data in clusters, characterized by small in-group and large out-group distances. We assume a wireless sensors network in which each sensor is given a large set of data and the objective is to provide a way to group the sensors in homogeneous clusters by information type. In previous literature, the desired number of clusters must be specified a priori by the user. In our approach, the clusters are constrained to have centroids with a distance at least ε between them and the number of desired clusters is not specified. Although traditional algorithms fail to solve the problem with this constraint, it can help obtain a better cluste…
Robust Synchronization-Based Graph Clustering
2013
Complex graph data now arises in various fields like social networks, protein-protein interaction networks, ecosystems, etc. To reveal the underlying patterns in graphs, an important task is to partition them into several meaningful clusters. The question is: how can we find the natural partitions of a complex graph which truly reflect the intrinsic patterns? In this paper, we propose RSGC, a novel approach to graph clustering. The key philosophy of RSGC is to consider graph clustering as a dynamic process towards synchronization. For each vertex, it is viewed as an oscillator and interacts with other vertices according to the graph connection information. During the process towards synchro…
Projector operators in clustering
2016
In a recent paper, the notion of quantum perceptron has been introduced in connection with projection operators. Here, we extend this idea, using these kind of operators to produce a clustering machine, that is, a framework that generates different clusters from a set of input data. Also, we consider what happens when the orthonormal bases first used in the definition of the projectors are replaced by frames and how these can be useful when trying to connect some noised signal to a given cluster. Copyright © 2016 John Wiley & Sons, Ltd.
The Burrows-Wheeler Transform between Data Compression and Combinatorics on Words
2013
The Burrows-Wheeler Transform (BWT) is a tool of fundamental importance in Data Compression and, recently, has found many applications well beyond its original purpose. The main goal of this paper is to highlight the mathematical and combinatorial properties on which the outstanding versatility of the $BWT$ is based, i.e. its reversibility and the clustering effect on the output. Such properties have aroused curiosity and fervent interest in the scientific world both for theoretical aspects and for practical effects. In particular, in this paper we are interested both to survey the theoretical research issues which, by taking their cue from Data Compression, have been developed in the conte…
Game of Thieves and WERW-Kpath: Two Novel Measures of Node and Edge Centrality for Mafia Networks
2021
Real-world complex systems can be modeled as homogeneous or heterogeneous graphs composed by nodes connected by edges. The importance of nodes and edges is formally described by a set of measures called centralities which are typically studied for graphs of small size. The proliferation of digital collection of data has led to huge graphs with billions of nodes and edges. For this reason, we focus on two new algorithms, Game of Thieves and WERW-Kpath which are computationally-light alternatives to the canonical centrality measures such as degree, node and edge betweenness, closeness and clustering. We explore the correlation among these measures using the Spearman’s correlation coefficient …
Online Induction of Probabilistic Real Time Automata
2012
Probabilistic real time automata (PRTAs) are a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps: the creation of the prefix tree acceptor (PTA) and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded data sets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing…
An ontological-based knowledge organization for bioinformatics workflow management system
2012
Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are E…
Active colloidal suspensions: Clustering and phase behavior
2014
We review recent experimental, numerical, and analytical results on active suspensions of self-propelled colloidal beads moving in (quasi) two dimensions. Active colloids form part of the larger theme of active matter, which is noted for the emergence of collective dynamic phenomena away from thermal equilibrium. Both in experiments and computer simulations, a separation into dense aggregates, i.e., clusters, and a dilute gas phase has been reported even when attractive interactions and an alignment mechanism are absent. Here, we describe three experimental setups, discuss the different propelling mechanisms, and summarize the evidence for phase separation. We then compare experimental obse…
Functional Brain Segmentation Using Inter-Subject Correlation in fMRI
2016
The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily‐life situations. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is h…
Comprehensive transcriptional analysis of the oxidative response in yeast
2008
The oxidative stress response in Saccharomyces cerevisiae has been analyzed by parallel determination of mRNA levels and transcription rates for the entire genome. A mathematical algorithm has been adapted for a dynamic situation such as the response to stress, to calculate theoretical mRNA decay rates from the experimental data. Yeast genes have been grouped into 25 clusters according to mRNA level and transcription rate kinetics, and average mRNA decay rates have been calculated for each cluster. In most of the genes, changes in one or both experimentally determined parameters occur during the stress response. 24% of the genes are transcriptionally induced without an increase inmRNAlevels…