the date of receipt and acceptance should be inserted later Abstract This paper proposes a novel application of Formal Concept Analysis (FCA) to neural decoding: the semantic relationships between the neural representations of large sets of stimuli are explored using concept lattices. In particular, the effects of neural code sparsity are modelled using the lattices. An exact Bayesian approach is employed to construct the formal context needed by FCA. This method is explained using an example of neurophysiological data from the high-level visual cortical area STSa. Prominent features of the resulting concept lattices are discussed, including indications for hierarchical face representation and a product-of-experts code in real neurons. The robustness of these features is illustrated by studying the effects of scaling the attributes.

Abstract not found

Abstract not found

This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published version ANDREWS, S. (2009). In-Close, a fast algorithm for computing formal concepts. In:

Abstract not found

Abstract. Business Intelligence solutions provide different means like OLAP, data mining or case based reasoning to explore data. Standard BI means are usually based on mathematical statistics and provide a quantitative analysis of the data. In this paper, a qualitative approach based on a mathematical theory called ”Formal Concept Analysis ” (FCA) is used instead. FCA allows clustering a given set of objects along attributes acting on the objects, hierarchically ordering those clusters, and finally visualizing the cluster hierarchy in so-called Hasse-diagrams. The approach in this paper is exemplified on a dataset of documents crawled from the SAP community network, which are persisted in a semantic triple store and evaluated with an existing FCA tool called ”ToscanaJ” which has been modified in order to retrieve its data from a triple store. 1 introduction Business Intelligence (BI) solutions provide different means like OLAP, data mining or case based reasoning to explore data. Standard BI means are usually designed to work with numerical data, thus they provide a quantitative analysis of the data (aka ”number crunching”) based on mathematical statistics. In fact, classical BI examples show ”accounting, finance, or some other calculationheavy subject ” [10]. To some extent, though arguably oversimplified, one can understand BI as acting on lists or tables filled with numbers. Compared to number crunching, Formal Concept Analysis (FCA) [3] provides a complementing approach. The starting point of FCA are crosstables (called ”formal contexts”), where the rows stand for some objects, the columns for some attributes, and the cells (intersections of rows and columns) carry the binary information whether an attribute applies to an object (usually indicated by a cross) or not. Based on this crosstable, the objects are clustered to meaningful sets. These clusters form a hierarchy, which can be visually displayed, e.g. by a so-called Hasse-diagram. A short introduction into FCA, as needed for this paper, is provided in the next section.

Abstract not found

Thoughts on exploiting instability in lattices for assessing the discrimination adequacy of a taxonomy

Formal concept analysis (FCA) has been applied successively in diverse fields such as data mining, conceptual modeling, social networks, software engineering, and the semantic web. One shortcoming of FCA, however, is the large number of concepts that typically arise in dense datasets hindering typical tasks such as rule generation and visualization. To overcome this shortcoming, it is important to develop formalisms and methods to segment, categorize and cluster formal concepts. The first step in achieving these aims is to define suitable similarity and dissimilarity measures of formal concepts. In this paper we propose three similarity measures based on existent set-based measures in addition to developing the completely novel zeros-induced measure. Moreover, we formally prove that all the measures proposed are indeed similarity measures and investigate the computational complexity of computing them. Finally, an extensive empirical evaluation on real-world data is presented in which the utility and character of each similarity measure is tested and evaluated. 1

Abstract. The definitive classification of scientific journals depends on their aim and scope details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were pre-processed by assigning each journal attributes defined by its topics. It is subsequently shown how theory of formal concept analysis can be applied to analyze the relations between journals and the extracted topics from their aims and scopes. It is shown how this approach can be used to facilitate the classifications of scientific journals. 1