Search results for "Clustering"

Weighted Clustering of Sparse Educational Data

2015

Clustering as an unsupervised technique is predominantly used in unweighted settings. In this paper, we present an efficient version of a robust clustering algorithm for sparse educational data that takes the weights, aligning a sample with the corresponding population, into account. The algorithm is utilized to divide the Finnish student population of PISA 2012 (the latest data from the Programme for International Student Assessment) into groups, according to their attitudes and perceptions towards mathematics, for which one third of the data is missing. Furthermore, necessary modifications of three cluster indices to reveal an appropriate number of groups are proposed and demonstrated. pe…

SparseHC: A Memory-efficient Online Hierarchical Clustering Algorithm

2014

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient online hierarchical clustering algorithm called SparseHC. SparseHC scans a sorted and possibly sparse distance matrix chunk-by-chunk. Meanwhile, a dendrogram is built by merging cluster pairs as and when the distance between them is determined to be the smallest among all remaining cluster pairs. The k…

Detecting clusters in spatially correlated waveforms

2017

Seismic networks often record signals characterized by similar shapes that provide important information according to their geographic positions. We propose an approach to identify homogeneous clusters of seismic waves, combining analysis of waveforms with metadata and spectrogram information. In waveforms clustering, cross-correlation measures between signals may presents some limitations, so we refer to more recent contributes relating data-depth based clustering analysis. The mechanism for alignment is also an important topic of the analysis: warping (or aligning) procedures identify nuisance effects in phase variation, that, if ignored, may result in a possible loss of information and t…

Models and methods for space and space-time interactions in complex point processes with applications on earthquakes

Spatio-temporal Dynamical Analysis of Brain Activity during Mental Fatigue Process

2021

Mental fatigue is a common phenomenon with implicit and multidimensional properties. It brings dynamic changes of functional brain networks. However, the challenging problem of false positives appears when the connectivity is estimated by Electroencephalography (EEG). In this paper, we propose a novel framework based on spatial clustering to explore the sources of mental fatigue and functional activity changes caused by them. To suppress the false positive observations, spatial clustering is implemented in brain networks. The nodes extracted by spatial clustering are registered back to functional magnetic resonance imaging (fMRI) source space to determined the sources of mental fatigue. The…

Intelligent solutions for real-life data-driven applications

2017

The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction models are selected based on information-theoretic measures and regression trees approach. The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral cluster…

An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach

2013

The purpose of this study is to examine the development of Italian tourist areas ( circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an…

Leveraging Users' Likes in a Video Streaming P2P Platform

2014

This paper investigates how a p2p television platform can take advantage of the presence of frequent channel viewers to grant them a more satisfying service than to less regular spectators. The idea we explore is to learn beforehand about the users' interests, in order to cluster them in groups that display different behaviors; then, the neighborhood creation strategy and video chunk scheduling algorithm of the overlay is altered, with the aim of serving frequent spectators in a privileged manner, providing them with a faster access to the selected channel without overly penalizing less habitual customers. An analytical model is developed, to capture the difference in startup delay that the…

Functional linear models for the analysis of similarity of waveforms

2018

In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and prop- agation pattern. Seismic waves can be considered as spatially interdependent three dimensional curves depending on time and the waveform similarity analysis can be configured as a functional clustering approach, on the basis of which the member- ship is assessed by the shape of the temporal patterns. For providing qualitative ex- traction of the most important information from the recorded signals we propose an integration of the metadata, related to the waves, as explicative variables of a func- tional linear models. The temporal pattern…

Modelling Systemic Cojumps with Hawkes Factor Models

2013

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.