500 research outputs found
Visual analytics in FCA-based clustering
Visual analytics is a subdomain of data analysis which combines both human
and machine analytical abilities and is applied mostly in decision-making and
data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was
developed to detect groups of objects with similar properties under similar
conditions. It is used in Social Network Analysis (SNA) and is a basis for
certain types of recommender systems. The problem of triclustering algorithms
is that they do not always produce meaningful clusters. This article describes
a specific triclustering algorithm and a prototype of a visual analytics
platform for working with obtained clusters. This tool is designed as a testing
frameworkis and is intended to help an analyst to grasp the results of
triclustering and recommender algorithms, and to make decisions on
meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on
Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary
Proceedings of the 3rd International Conference on Analysis of Images, Social
Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201
A conceptual approach to gene expression analysis enhanced by visual analytics
The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts
Visualization and analytics of codicological data of Hebrew books
The goal is to provide a proper data model, using a common vocabulary, to
decrease the heterogenous nature of these datasets as well as its inherent uncertainty
caused by the descriptive nature of the field of Codicology. This research project was
developed with the goal of applying data visualization and data mining techniques to the
field of Codicology and Digital Humanities. Using Hebrew manuscript data as a starting
point, this dissertation proposes an environment for exploratory analysis to be used by
Humanities experts to deepen their understanding of codicological data, to formulate new,
or verify existing, research hypotheses, and to communicate their findings in a richer way.
To improve the scope of visualizations and knowledge discovery we will try to use data
mining methods such as Association Rule Mining and Formal Concept Analysis. The
present dissertation aims to retrieve information and structure from Hebrew manuscripts
collected by codicologists. These manuscripts reflect the production of books of a specific
region, namely "Sefarad" region, within the period between 10th and 16th.A presente dissertação tem como objetivo obter conhecimento estruturado de
manuscritos hebraicos coletados por codicologistas. Estes manuscritos refletem a
produção de livros de uma região específica, nomeadamente a região "Sefarad", no
período entre os séculos X e XVI. O objetivo é fornecer um modelo de dados apropriado,
usando um vocabulário comum, para diminuir a natureza heterogénea desses conjuntos
de dados, bem como sua incerteza inerente causada pela natureza descritiva no campo da
Codicologia. Este projeto de investigação foi desenvolvido com o objetivo de aplicar
técnicas de visualização de dados e "data mining" no campo da Codicologia e Humanidades
Digitais. Usando os dados de manuscritos hebraicos como ponto de partida, esta
dissertação propõe um ambiente para análise exploratória a ser utilizado por especialistas
em Humanidades Digitais e Codicologia para aprofundar a compreensão dos dados
codicológicos, formular novas hipóteses de pesquisa, ou verificar existentes, e comunicar
as suas descobertas de uma forma mais rica. Para melhorar as visualizações e descoberta
de conhecimento, tentaremos usar métodos de data mining, como a "Association Rule
Mining" e "Formal Concept Analysis"
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Microblogging services like Twitter and Facebook collect millions of user
generated content every moment about trending news, occurring events, and so
on. Nevertheless, it is really a nightmare to find information of interest
through the huge amount of available posts that are often noise and redundant.
In general, social media analytics services have caught increasing attention
from both side research and industry. Specifically, the dynamic context of
microblogging requires to manage not only meaning of information but also the
evolution of knowledge over the timeline. This work defines Time Aware
Knowledge Extraction (briefly TAKE) methodology that relies on temporal
extension of Fuzzy Formal Concept Analysis. In particular, a microblog
summarization algorithm has been defined filtering the concepts organized by
TAKE in a time-dependent hierarchy. The algorithm addresses topic-based
summarization on Twitter. Besides considering the timing of the concepts,
another distinguish feature of the proposed microblog summarization framework
is the possibility to have more or less detailed summary, according to the
user's needs, with good levels of quality and completeness as highlighted in
the experimental results.Comment: 33 pages, 10 figure
Combining Business Intelligence with Semantic Technologies: The CUBIST Project
This paper describes the European Framework Seven CUBIST project, which ran from October 2010 to September 2013. The project aimed to combine the best elements of traditional BI with the newer, semantic, technologies of the Sematic Web, in the form of RDF and FCA. CUBIST’s purpose was to provide end-users with "conceptually relevant and user friendly visual analytics" to allow them to explore their data in new ways, discovering hidden meaning and solving hitherto difficult problems. To this end, three of the partners in CUBIST were use-cases: recruitment consultancy, computational biology and the space industry. Each use-case provided their own requirements and evaluated how well the CUBIST outcomes addressed them
The CUBIST Project: Combining and Uniting Business Intelligence with Semantic Technologies
As a preface to this Special 'CUBIST' Edition of the International Journal of Intelligent Information Technologies IJIIT, this article describes the European Framework Seven Combining and Unifying Business Intelligence with Semantic Technologies CUBIST project, which ran from October 2010 to September 2013. The project aimed to combine the best elements of traditional BI with the newer, semantic, technologies of the Sematic Web, in the form of the Resource Description Framework RDF, and Formal Concept Analysis FCA. CUBIST's purpose was to provide end-users with "conceptually relevant and user friendly visual analytics" to allow them to explore their data in new ways, discovering hidden meaning and solving hitherto difficult problems. To this end, three of the partners in CUBIST were use-cases: recruitment consultancy, computational biology and the space industry. Each use-case provided their own requirements and problems that were finally addressed by the prototype CUBIST visual-analytics developed in the project
A visual analytics technique for exploring gene expression in the developing mouse embryo
This paper describes a novel visual analytics technique for exploring gene co-expression is the developing mouse embryo. The majority of existing techniques either visualise a single gene profile or a single tissue profile, whereas the technique presented here combines both - visualising gene co-expression in a group of tissues, for example, in the components of the developing heart. The technique is presented using data provided by the Edinburgh Mouse Atlas Project of gene expression assays conducted on tissues of the developing mouse embryo and a corresponding hierarchical graph of tissues defining the mouse anatomy. By specifying a particular tissue, such as the heart, and a particular stage of development, a Formal Concept Lattice is constructed making use of the hierarchical mouse anatomy to visualise the components of the specified tissue and the genes expressed in each component. Examples of lattices are given to illustrate the technique and show how it can provide useful information to genetic researchers of embryo development and tissue differentiation, particularly when comparing gene expression across several stages of development
Curbing domestic violence: instantiating C-K theory with formal concept analysis and emergent self organizing maps.
In this paper we propose a human-centered process for knowledge discovery from unstructured text that makes use of Formal Concept Analysis and Emergent Self Organizing Maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the Concept-Knowledge (C-K) theory design square. To illustrate its effectiveness, we report on a real-life case study of using the process at the Amsterdam-Amstelland police in the Netherlands aimed at distilling concepts to identify domestic violence from the unstructured text in actual police reports. The case study allows us to show how the process was not only able to uncover the nature of a phenomenon such as domestic violence, but also enabled analysts to identify many types of anomalies in the practice of policing. We will illustrate how the insights obtained from this exercise resulted in major improvements in the management of domestic violence cases.Formal concept analysis; Emergent self organizing map; C-K theory; Text mining; Actionable knowledge discovery; Domestic violence;
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