Search results for "Clusterin"

Comparison of cluster validation indices with missing data

2018

Clustering is an unsupervised machine learning technique, which aims to divide a given set of data into subsets. The number of hidden groups in cluster analysis is not always obvious and, for this purpose, various cluster validation indices have been suggested. Recently some studies reviewing validation indices have been provided, but any experiments against missing data are not yet available. In this paper, performance of ten well-known indices on ten synthetic data sets with various ratios of missing values is measured using squared euclidean and city block distances based clustering. The original indices are modified for a city block distance in a novel way. Experiments illustrate the di…

Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space

2019

In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results. peerReviewed

Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

2019

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions.

The Diversification of Sicilian Farms: A Way to Sustainable Rural Development

2021

Rural areas still suffer from a lack of sustainable development, and the diversification of farms may be a step in the right direction. The paper provides a detailed picture of the diversification of Sicilian farms into tourism services. Specifically, we propose a simple indicator of localization intensity of agritourism farms and explore their spatial distribution at municipality level. Our study highlights that Sicilian farms rarely diversify into tourism services, despite being situated in attractive areas. That said, some significant spatial clusters of municipalities where agritourism farms are highly concentrated do emerge from the study.

Anomaly-based online intrusion detection system as a sensor for cyber security situational awareness system

2016

Almost all the organisations and even individuals rely on complex structures of data networks and networked computer systems. That complex data ensemble, the cyber domain, provides great opportunities, but at the same time it offers many possible attack vectors that can be abused for cyber vandalism, cyber crime, cyber espionage or cyber terrorism. Those threats produce requirements for cyber security situational awareness and intrusion detection capability. This dissertation concentrates on research and development of anomaly-based network intrusion detection system as a sensor for a situational awareness system. In this dissertation, several models of intrusion detection systems are devel…

Earthquakes clustering based on maximum likelihood estimation of point process conditional intensity function

2006

Hierarchical Analysis of Forms of Support for Employees in the Field of Health Protection and Quality of Work during the COVID-19 Pandemic and the De…

2022

Issues of employee support during the COVID-19 pandemic and the post-pandemic period are of an interdisciplinary nature. Moreover, these should be considered from both an epistemological and a practical perspective. The aim of this study was to determine what forms of support for employees in terms of health and quality of work were provided by employers during the pandemic and what forms of support will be expected by employees after it ceases. The research process was carried out in two stages: primary and secondary exploration and quantitative clarification. In the first stage, a systematic review of the literature and a critical analysis of the so-called grey literature was performed. I…

Clustering and interorganizational dynamics in foreign market entry strategies. Evidence from Chinese MNEs

2015

Owing to the globalization and economic integration worldwide, countries and markets become more independent, and companies and people are all inevitably involved in the global marketplace. This situation increases the importance of international management (IM) in nowadays business practice and the relevance of study on it. The essential focus of IM research is on multinational enterprise (MNE) strategies, among which the foreign market entry strategies have been underlined and received a great deal of attention. The carried out research aims to increase the knowledge on MNE’s behaviors and strategic decisions in foreign direct investment (FDI). We first gave a retrospective look at foreig…

Machine learning for mortality analysis in patients with COVID-19

2020

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…

Empirical Study on the Relationship between the Cross-Correlation among Stocks and the Stocks' Volatility Clustering

2013

In this paper we discuss univariate and multivariate statistical properties of volatility with the aim of understanding how these two aspects are interrelated. Specifically, we focus on the relationship between the cross-correlation among stock's volatilities and the volatility clustering. Volatility clustering is related to the memory property of the volatility time-series and therefore to its predictability. Our results show that there exists a relationship between the level of predictability of any volatility time-series and the amount of its inter-dependence with other assets. In all considered cases, the more the asset is linked to other assets, the more its volatility keeps memory of …