Search results for "Clusterin"
showing 10 items of 478 documents
Hierarchically nested factor model from multivariate data
2005
We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.
Prototype-based learning on concept-drifting data streams
2014
Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To ide…
Fuzzy technique for microcalcifications clustering in digital mammograms
2012
Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …
Multispectral imaging and its use for face recognition : sensory data enhancement
2015
In this thesis, we focus on multispectral image for face recognition. With such application,the quality of the image is an important factor that affects the accuracy of therecognition. However, the sensory data are in general corrupted by noise. Thus, wepropose several denoising algorithms that are able to ensure a good tradeoff betweennoise removal and details preservation. Furthermore, characterizing regions and detailsof the face can improve recognition. We focus also in this thesis on multispectral imagesegmentation particularly clustering techniques and cluster analysis. The effectiveness ofthe proposed algorithms is illustrated by comparing them with state-of-the-art methodsusing both…
Statistical analysis techniques for Partial Discharges measurement under DC voltage
2022
Partial discharges (PD) phenomenon for HVAC (High-Voltage-Alternating-Current) systems has already been widely studied in literature and from this it has been possible to produce a regulatory document that provides indications for description and identification of the different types of discharges. However, the growing diffusion of HVDC (High-Voltage-Direct-Current) systems makes necessary the development of analysis techniques also for DC case. In this paper data obtained from PD measurement under DC voltage were analyzed through the time-frequency map combined with a density-based clustering algorithm. The results show that, with this approach, it's possible to perform a noise rejection a…
Space-Time FPCA Clustering of Multidimensional Curves.
2018
In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.
Depth-based methods for clustering of functional data.
2017
The problem of detecting clusters is a common issue in the analysis of functional data and some interesting intuitions from approaches relied on depth measures can be considered for construction of basic tools for clustering of curves. Motivated by recent contributions on the problem clustering and alignment of functional data, we also consider the problem of aligning a set of curves when classification procedures are implemented. The variability among curves can be interpreted in terms of two components, phase and amplitude; phase variability, or misalignment, can be eliminated by aligning the curves, according to a similarity index and a warping function. Some approaches address the misal…
Burrows-Wheeler transform and Run-Length Enconding
2017
In this paper we study the clustering effect of the Burrows-Wheeler Transform (BWT) from a combinatorial viewpoint. In particular, given a word w we define the BWT-clustering ratio of w as the ratio between the number of clusters produced by BWT and the number of the clusters of w. The number of clusters of a word is measured by its Run-Length Encoding. We show that the BWT-clustering ratio ranges in ]0, 2]. Moreover, given a rational number \(r\,\in \,]0,2]\), it is possible to find infinitely many words having BWT-clustering ratio equal to r. Finally, we show how the words can be classified according to their BWT-clustering ratio. The behavior of such a parameter is studied for very well-…
A family of distances for preference–approvals
2022
Producción Científica
Dsdivn: A Distributed Software-Defined Networking Architecture for Infrastructure-Less Vehicular Networks
2017
International audience; In the last few years, the emerging network architecture paradigm of Software-Defined Networking (SDN), has become one of the most important technology to manage large scale networks such as Vehicular Ad-hoc Networks (VANETs). Recently, several works have shown interest in the use of SDN paradigm in VANETs. SDN brings flexibility, scalability and management facility to current VANETs. However, almost all of proposed Software-Defined VANET (SDVN) architectures are infrastructure-based. This paper will focus on how to enable SDN in infrastructure-less vehicular environments. For this aim, we propose a novel distributed SDN-based architecture for uncovered infrastructur…