The major disadvantage of hierarchical clustering in fMRI data analysis is that an appropriate clustering threshold needs to be specified. Upon grouping data into a hierarchical tree, clusters are identified either by specifying their number or by choosing an appropriate inconsistency coefficient. Since the number of clusters present in the data is not known beforehand, even a slight variation ...

We consider the Kolmogorov operator associated with a reaction-diffusion equation having polynomially growing reaction coefficient and perturbed by a noise of multiplicative type, in the Banach space E of continuous functions. By analyzing the smoothing properties of the associated transition semigroup, we prove a modification of the classical identité du carré des champs that applies to the pr...

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms in for clustering. LEACH algorithm is one of the important clusters based in wireless sensor network. In this paper we proposed a new clustering method for increasing of network lifetime. In this we distribute several sensors with a high-energy for managing the clus...

Milgram Condition proposed by Aoyama et al. [11] plays an important role on the analysis of ”six degrees of separation”. We have shown[16], [17] that the relations between Milgram condition and the generalized clustering coefficient, which was introduced as an index for measuring the number of closed paths by us[6]-[10], are absolutely different in scale free networks [12] and small world netwo...

OBJECTIVES This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. MATERIALS AND METHODS The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indic...

Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers. Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility. In the present paper, we study the divisibility of natural numbers using the framework of a growing complex netw...

We show that strong clustering of links in complex networks, i.e., a high probability of triadic closure, can induce a localization-delocalization quantum phase transition (Anderson-like transition) of coherent excitations. For example, the propagation of light wave packets between two distant nodes of an optical network (composed of fibers and beam splitters) will be absent if the fraction of ...

The paper deals with selected similarity measures which can be used for hierarchical clustering of nominal variables. These variables are commonly used in questionnaire surveys. Cluster analysis can be applied in case a reduction of a dataset size is welcomed. In this paper, there are examined several similarity measures for nominal variable clustering, which have been introduced in recent year...

Previous work has revealed that the synchronizability of a scale-free network tends to be suppressed when its clustering coefficient is increased. We present a theory to explain this phenomenon. Our proposition is that, as the network becomes more strongly clustered, topological loop structure can emerge, generating a set of eigenvalues that are close to zero. As a result, the dynamics of synch...

In this paper we present a hybrid approach based on combining fuzzy clustering, seed region growing, and Jaccard similarity coefficient algorithms to measure gray (GM) and white matter tissue (WM) volumes from magnetic resonance images (MRIs). The proposed algorithm incorporates intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It sta...