Search results for "cluster analysis."
showing 10 items of 805 documents
Approximation of Pore Space with Ellipsoids: A Comparison of a Geometrical Method with a Statistical one
2018
We work with tomographic images of pore space in soil. The images have large dimensions and so in order to speed-up biological simulations (as drainage or diffusion process in soil), we want to describe the pore space with a number of geometrical primitives significantly smaller than the number of voxels in pore space. In this paper, we use the curve skeleton of a volume to segment it into some regions. We describe the method to compute the curve skeleton and to segment it with a simple segment approximation. We approximate each obtained region with an ellipsoid. The set of final ellipsoids represents the geometry of pore space and will be used in future simulations. We compare this method …
Coworking spaces and makerspaces: Mapping the state of research
2022
Coworking and its merits and benefits have been under heavy scholarly investigation. Also in practice, the phenomenon with its characteristics and manifestations becomes increasingly relevant on many levels and for many different types of people and organizations. But why is that so, and how are the research activities distributed between researchers, countries, and journals? To answer these questions, we first analyzed existing literature and extracted the focal points of the respective approaches. We conducted a cluster analysis on the existing literature by analyzing data from the Web of Science. With these clusters, we show the development of the research stream and how the studies are …
New statistical post processing approach for precise fault and defect localization in TRI database acquired on complex VLSI
2013
International audience; Timing issue, missing or extra state transitions or unusual consumption can be detected and localized by Time Resolved Imaging (TRI) database analysis. Although, long test pattern can challenge this process. The number of photons to process rapidly increases and the acquisition time to have a good signal over noise ratio (SNR) can be prohibitive. As a result, the tracking of the defect emission signature inside a huge database can be quite complicated. In this paper, a method based on data mining techniques is suggested to help the TRI end user to have a good idea about where to start a deeper analysis of the integrated circuit, even with such complex databases.
Forms and Functions of the Real Estate Market of Palermo (Italy). Science and Knowledge in the Cluster Analysis Approach
2016
The analysis of the housing market of a city requires suitable approaches and tools, such as data mining models, to represent its complexity which derives on many elements, e.g. the type of capital asset-house is a common good and an investment good as well, the heterogeneity of the urban areas—each of them has own historical and representative values and different urban functions—and the variability of building quality. The housing market of the most densely populated area of Palermo (Italy), corresponding to ten districts, is analyzed to verify the degree of its inner homogeneity and the relations between the quality of the characteristics and the price of the properties. Five hundred set…
Entrepreneurship and Resilience in Spanish Sports Clubs: A Cluster Analysis.
2021
Entrepreneurial orientation can be an effective response by sports clubs to manage a recession, such as the COVID-19 crisis. Therefore, its study can be fundamental to understand different ways of managing a recession. This study analyzes the entrepreneurial orientation of Spanish non-profit sports clubs to identify different groups and their profiles. The sample is composed of 145 Spanish non-profit sports clubs. Different validated scales have been used to analyze entrepreneurial orientation, business model adaptation, service quality, and economic and social performance (performance in social impact and performance in social causes). Entrepreneurial orientation is the variable used to di…
Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries
2018
The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97–…
Assessment of computational methods for the analysis of single-cell ATAC-seq data
2019
Abstract Background Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. Results We present a benchmarking framework that …
CUDA-Accelerated Alignment of Subsequences in Streamed Time Series Data
2014
Euclidean Distance (ED) and Dynamic Time Warping (DTW) are cornerstones in the field of time series data mining. Many high-level algorithms like kNN-classification, clustering or anomaly detection make excessive use of these distance measures as subroutines. Furthermore, the vast growth of recorded data produced by automated monitoring systems or integrated sensors establishes the need for efficient implementations. In this paper, we introduce linear memory parallelization schemes for the alignment of a given query Q in a stream of time series data S for both ED and DTW using CUDA-enabled accelerators. The ED parallelization features a log-linear calculation scheme in contrast to the naive …
Data Mining Algorithms for Knowledge Extraction
2020
In this paper, we study the methods, techniques, and algorithms used in data mining, and from the studied algorithms, we emphasized the clustering algorithms, more precisely on the K-means algorithm. This algorithm was first studied using the Euclidean distance, then modifying the distance between the clusters using the distances Mahalanobis and Canberra. After implementing the algorithms in C/C++, we compared the clustering of the three algorithms, after which we modified them and studied the distance between the clusters.
Towards Evidence-Based Academic Advising Using Learning Analytics
2018
Academic advising is a process between the advisee, adviser and the academic institution which provides the degree requirements and courses contained in it. Content-wise planning and management of the student’ study path, guidance on studies and academic career support is the main joint activity of advising. The purpose of this article is to propose the use of learning analytics methods, more precisely robust clustering, for creation of groups of actual study profiles of students. This allows academic advisers to provide evidence-based information on the study paths that have actually happened similarly to individual students. Moreover, academic institutions can focus on management and upda…