Abstract. FcaBedrock employs user-guided automation to convert c.s.v. data sets into Burmeister .cxt and FIMI .dat context files for FCA.
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.
The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the Athena project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State of the art technology such as formal concept analysis and machine learning is used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment and assignment of priority. We present an evaluation of results obtained from a set of tweets which emerged from the Colorado wild fires of 2012.
Abstract-Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called Formal Concept Analysis (FCA). This paper describes a tool called FcaBedrock that converts data into Formal Contexts for FCA. The paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Creating Formal Contexts using FcaBedrock is shown to be straightforward and versatile. Large data sets are easily converted into a standard FCA format.
Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as consistent when contradiction associated with the data is not visualised. This work identifies incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis (FCA) tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data (IID) when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose objects are associated with mutually exclusive many-valued attributes, to a formal context.
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