Search results for "Clusterin"
showing 10 items of 478 documents
An Empirical Study of the Relation Between Community Structure and Transitivity
2012
One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and loosely interconnected groups of nodes called communities. In an attempt to better understand this concept, we study the relationship between the strength of the community structure and the network transitivity (or clustering coefficient). Although intuitively appealing, this analysis was not performed before. We adopt an approach based on random models to empirically study how one property varies depending on the other. It turns out the transitivity increases with the community structure strength, and is also affected by the distribution of the community sizes. Furthermore, …
Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering
2018
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their average. Distance vector of each superpixel from the average is computed in principal components coordin…
Clustering of waveforms-data based on FPCA direction
2010
The necessity of nding similar features of waveforms data recorded for earthquakes at di erent time instants is here considered, since eventual similarity between these functions could suggest similar behavior of the source process of the corresponding earthquakes. In this paper we develop a clustering algorithm for curves based on directions de ned by an application of PCA to functional data.
Clustering of waveforms based on FPCA direction
2010
Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). Waveforms correlation techniques have been introduced to charac- terize the degree of seismic event similarity (Menke, 1999) and in facilitating more accurate relative locations within similar event clusters by providing more precise timing of seismic wave (P and S) arrivals (Phillips, 1997). In this paper functional analysis (Ramsey, and Silverman, 2006) is considered to highlight common characteristics of waveforms-data and to summarize these charac- teristics by few components, by applying a variant of a classical clust…
Space-time FPCA Algorithm for clustering of multidimensional curves.
2016
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.
Functional Principal components direction to cluster earthquake waveforms
2010
Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical clustering method to rotated data, according to the direction of maximum variance. A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that require previous interpolation of data based on splines or linear fitting (Garc´ıa- Escudero and Gordali…
FPCA Algorithm For Waveform Clustering
2011
Similar features between waveform data recorded for earthquakes at different time instants could suggest similar behavior of the source process of the corresponding source seismic process. In this paper we combine the aim of finding clusters from a set of individual waveform curves with the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. This approach overcome the limitation of the cross-correlation, and represents an alternative to methods based on the interpolation of data by splines or linear fitting.
Plaid model for microarray data: an enhancement of the pruning step
2010
Microarrays have become a standard tool for studying gene functions. For example, we can investigate if a subset of genes shows a coherent expression pattern under different conditions. The plaid model, a model-based biclustering method, can be used to incorporate the addiction structure used for the microarray experiment. In this paper we describe an enhancement for the plaid model algorithm based on the theory of the false discovery rate.
Simultaneous seismic wave clustering and registration
2012
In this paper we introduce a simple procedure to identify clusters of multivariate waveforms based on a simultaneous assignation and alignment procedure. This approach is aimed at the identification of clusters of earthquakes, assuming that similarities between seismic events with respect to hypocentral parameters and focal mechanism correspond to similarities between waveforms of events. Therefore we define a distance measure between seismic curves in R^d d>=1, in order to interpret and better understand the main features of the generating seismic process.
A New Dissimilarity Measure for Clustering Seismic Signals
2011
Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities to identify clusters of seismic events. In this work we propose a new dissimilarity measure between seismic signals whose reliability has been tested on real seismic dat…