Search results for "Cluster Analysis"
showing 10 items of 848 documents
An Inquiry-Based Approach to a Pedagogical Laboratory for Primary School Teacher Education
2017
In questo articolo vengono presentati e di- scussi alcuni risultati relativi alla sperimen- tazione di due esperienze di didattica laboratoriale della fisica, una basata su me- todi di indagine scientifica e l’altra su meto- dologie didattiche più “tradizionali”, svolte durante l’A.A. 2014-15 con studenti del CdL in Scienze della Formazione Primaria del- l’Università di Palermo. I dati, analizzati tra- mite metodi quantitativi, sono stati ricavati dalla somministrazione prima, durante e do- po le attività laboratoriali, di un questionario finalizzato a comprendere gli stili di insegna- mento preferiti dagli studenti, la motivazio- ne di questi all’apprendimento/insegna- mento delle scienze …
Multidimensional Scaling in Cluster Analysis: examples in Science and Mathematics Education
2021
Several researches in STEM education research highlight the advantages of an inte- grated approach to these disciplines that relates knowledge and know-how, design and implementation, theoretical and practical problems [5, 4, 6]. In some researches, the effectiveness of these approaches on students conceptual understanding and motivation and has been studied through the use of quantitative analysis tools such as cluster analysis (CLA) [1, 7]. Through CLA it is possible to characterize students analyzing the strategies they deploy to tackle, for example, questionnaires built so as to investigate the lines of reasoning implemented by them when they are proposed with problematic situations. In…
A Phenomenological Study About the Effect of Covid-19 Pandemic on the Use of Teaching Resources in Mathematics
2023
In this contribution, we discuss phenomenological research related to a pilot study carried out by the Consortium of the MaTeK Horizon 2020 project during the 2020–21 academic year. The research aims to analyse the effects of the Covid-19 pandemic on the use of teaching resources in mathematics in five coun- tries. A questionnaire made of seven questions was administered to a data sample made of teachers of all grades. The answers coming from the questionnaire were quantitatively and qualitatively analysed. Closed-ended questions were analysed by using a clustering methodology called k-means. Open-ended questions were qualitatively analysed. The results show that almost all the teachers are…
Cluster analysis of HVSR peak datasets to detect geological structures
2014
A modified centroid-based algorithm has been applied to HVSR (Horizontal to Vertical Spectral Ratio) datasets (Nakamura, 2000) acquired for studies of seismic microzoning in various urban centers of Sicilian towns also aimed to obtain detailed reconstruction of the roof of the seismic bedrock (Di Stefano et al. 2014). HVSR data were previously properly processed to extract frequency and amplitude of peaks by a code based on clustering of HVSR curves determined in sliding time windows. In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set. After fixing the number of clusters, the algorithm find the cluster centers an…
Statistical approach for cavity detection using seismic refraction and electrical resistivity data
2017
To test limitations and effectiveness of seismic tomography when coupled to geoelectrical technique for cavity detection 2D synthetic models were used. Synthetic models were created with different number of cavity and blocks of highly cohesive lithological material (high seismic velocity and resistivity values). A modified version of multiple gradient (Martorana et al., 2016) has been used for electrical sequence. The cluster analysis performed on static units defined by electrical resistivity values, P wave velocities, and seismic density on coincident sections, allowed to interpret subsoil structures. The use of the non-hierarchical clustering algorithm has been chosen because it is less …
Image Segmentation based on Genetic Algorithms Combination
2005
The paper describes a new image segmentation algorithm called Combined Genetic segmentation which is based on a genetic algorithm. Here, the segmentation is considered as a clustering of pixels and a similarity function based on spatial and intensity pixel features is used. The proposed methodology starts from the assumption that an image segmentation problem can be treated as a Global Optimization Problem. The results of the image segmentations algorithm has been compared with recent existing techniques. Several experiments, performed on real images, show good performances of our approach compared to other existing methods.
Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis
2012
AbstractThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of pre…
Dissimilarity Measures for the Identification of Earthquake Focal Mechanisms
2013
This work presents a study about dissimilarity measures for seismic signals, and their relation to clustering in the particular problem of the identification of earthquake focal mechanisms, i.e. the physical phenomena which have generated an earthquake. Starting from the assumption that waveform similarity implies similarity in the focal parameters, important details about them can be determined by studying waveforms related to the wave field produced by earthquakes and recorded by a seismic network. Focal mechanisms identification is currently investigated by clustering of seismic events, using mainly cross-correlation dissimilarity in conjunction with hierarchical clustering algorithm. By…
Speeding up the Consensus Clustering methodology for microarray data analysis
2010
Abstract Background The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose…
Simulated Annealing Technique for Fast Learning of SOM Networks
2011
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…