This paper describes the probabilistic behaviour of a random Sturmian word. It performs the probabilistic analysis of the recurrence function which can be viewed as a waiting time to discover all the factors of length n of the Sturmian word. This parameter is central to combinatorics of words. Having fixed a possible length n for the factors, we let α to be drawn uniformly from the unit interva...

Co-clustering aims at simultaneously partitioning both dimensions of a data matrix. It has demonstrated better performances than one-sided clustering for high-dimensional data. The Latent Block Model (LBM) is probabilistic model co-clustering based on mixture models that proven useful broad class In this paper, we propose to leverage prior knowledge in the form pairwise semi-supervision row and...

Clustering problem is one of the significant issues for wireless sensor networks concerned with energy consumption and large-scale deployment. Several energy-efficient clustering algorithms have been proposed to improve the energy utilization efficiency and prolong the network lifetime. In this paper, we propose a new clustering scheme after a comprehensive analysis on existing protocols. In ou...

this paper presents a comparative study between three versions of adaptive neuro-fuzzy inference system (anfis) algorithms and a pseudo-forward equation (pfe) to characterize the north sea reservoir (f3 block) based on seismic data. according to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

Wireless sensor network (WSN) is a collection of smart sensor nodes cooperated together for achieving the desire of the assigned application. However, these nodes suffer from different limitations, including limited energy sources and limited processing capabilities. Clustering and data aggregation are considered main solutions for prolonging the network lifetime. Clustering is either based on ...

The December 2004 Sumatra-Andaman earthquake emphasized the need for a consistent and comprehensive assessment of tsunami hazard. We have developed a method for Probabilistic Tsunami Hazard Analysis (PTHA) based on the traditional Probabilistic Seismic Hazard Analysis (PSHA) and therefore completely consistent with standard seismic practice. In lieu of attenuation relations, it uses the summati...

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, model-based clustering techniques usually perform poorly when dealing with high-dimensional data streams, which are nowadays a frequent data type. To overcome this limitation of model-based clustering, we propose an online inference algorithm for the mixture of probabilisti...

Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithms (e.g., clustering, conditioning) are e ective only if the problem has a sparse graph captured by parameters such as tree width and cycle-cutset size. In this paper we initiate a study to determine the potential of structure-based algorithms in real-life a...