Search results for "Mining"

Characterisation of clayey raw materials for ceramic manufacture in ancient Sicily

2011

Abstract It is well known that mineralogical, petrographic and chemical analyses can successfully support provenance studies of archaeological ceramics. The characterisation of a ceramic paste, the assessment of its technology of production and its provenance assignment related to a specific production centre or to a geographical and/or compositional space, are all tasks that can be performed even more accurately if the clayey raw materials used in pottery production are also studied. For that reason the identification of the clay deposits exploited in a given ceramic workshop often plays a key role in the archaeometric reconstruction of the production cycle where geology, mineralogy, petro…

7 Tesla MRI will soon be helpful to guide clinical practice in multiple sclerosis centres – No

2021

Fighting the bushfire in HCC trials

2011

Surgical clinical trials—need for international collaboration

2013

The Urban Landscape and the Real Estate Market. Structures and Fragments of the Axiological Tessitura in a Wide Urban Area of Palermo

2016

The proposed study deals with the urban landscape of Palermo and its possible representation from the perspective of the real estate market analysis. Real estate is one of the most significant types of capital asset and the wide range of its possible utilizations makes complex the interpretation of the market phenomena. The multi-layered reality of such a large city (represented through the sample of 500 properties) needs to be articulated into a significant set of sub-markets in order to outline the complexity and to map the distribution of homogeneous groups of properties within the whole city area. The comparison between quality and price within each cluster allows us to elicit the degre…

SMART: Unique splitting-while-merging framework for gene clustering

2014

© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …

Computation Cluster Validation in the Big Data Era

2017

Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis.

A local complexity based combination method for decision forests trained with high-dimensional data

2012

Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how thi…

GenClust: A genetic algorithm for clustering gene expression data

2005

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …

Data Analysis and Bioinformatics

2007

Data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues. Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning. Data mining adds to clustering the complications of very large data-sets with many attributes of different types. And this is a typical situation in biology. Some cases studies are also described.