Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

Document retrieval for e-mail search and discovery using Formal Concept Analysis

This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual. le structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available in e-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.

Concept analysis in social studies

This project was undertaken because of a need to analyse concepts in social science more specifically and sequence them more carefully in a social science program. Concepts have been identified vaguely on many curriculum documents or left in isolation from each other when they are specifically identified. The project's aim was to identify a method for analysing concepts and sequencing their teaching on some rational basis. Once the method for analysing concepts was identified a questionnaire was designed and administered to a random sample of students at the grade three, five and eight levels. The questionnaire attempted to measure their comprehension of specific social science concepts at several levels which became progressively more complex. The major hypothesis was that there would be a direct correlation between age and achievement on the questionnaire. The raw scores were seriated and correlated with the ages of the students using the rankdifference- squared method. For the majority of areas tested it was found that there was a significant correlation between age and achievement on the questionnaire. Variation in the correlation coefficients generated suggests that comprehension of social science concepts is not simply a function of age but is probably a function of several inter-related factors such as reading ability, skill in Basic Thinking Skills and age. Thirty students completed each test. There were three tests in the questionnaire.

Exploration tools in formal concept analysis

The development of conceptual knowledge systems specifically requests knowledge acquisition tools within the framework of formal concept analysis. In this paper, the existing tools are presented, and furhter developments are discussed.

Conceptual structures represented by conceptual graphs and formal concept analysis

Conceptual Graphs and Formal Concept Analysis have in common basic concerns: the focus on conceptual structures, the use of diagrams for supporting communication, the orientation by Peirce's Pragmatism, and the aim of representing and processing knowledge. These concerns open rich possibilities of interplay and integration. We discuss the philosophical foundations of both disciplines, and analyze their specific qualities. Based on this analysis, we discuss some possible approaches of interplay and integration.

Intelligent structuring and reducing of association rules with formal concept analysis

Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present relatively small bases for association rules from which all rules can be deduced. We also provide algorithms for their calculation.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Conceptual knowledge processing with formal concept analysis and ontologies

Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.

Ontology merging with formal concept analysis

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. In this short paper, we address two methods for merging ontologies based on Formal Concept Analysis: FCA-Merge and ONTEX. --- FCA-Merge is a method for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-Merge. The generated result is then explored and transformed into the merged ontology with human interaction. --- ONTEX is a method for systematically structuring the top-down level of ontologies. It is based on an interactive, top-down- knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.

Efficient mining of association rules based on formal concept analysis

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

Sustainability in nursing : a concept analysis

Aim. The aim of this study was to describe, explore and explain the concept of sustainability in nursing. Background. Although researchers in nursing and medicine have emphasised the issue of sustainability and health, the concept of sustainability in nursing is undefined and poorly researched. A need exists for theoretical and empirical studies of sustainability in nursing. Design. Concept analysis as developed by Walker and Avant. Method. Data were derived from dictionaries, international healthcare organisations and literature searches in the CINAHL and MEDLINE databases. Inclusive years for the search ranged from 1990 to 2012. A total of fourteen articles were found that referred to sustainability in nursing. Results. Sustainability in nursing involves six defining attributes: ecology, environment, future, globalism, holism and maintenance. Antecedents of sustainability require climate change, environmental impact and awareness, confidence in the future, responsibility and a willingness to change. Consequences of sustainability in nursing include education in the areas of ecology, environment and sustainable development as well as sustainability as a part of nursing academic programs and in the description of the academic subject of nursing. Sustainability should also be part of national and international healthcare organisations. The concept was clarified herein by giving it a definition. Conclusion. Sustainability in nursing was explored and found to contribute to sustainable development, with the ultimate goal of maintaining an environment that does not harm current and future generations' opportunities for good health. This concept analysis provides recommendations for the healthcare sector to incorporate sustainability and provides recommendations for future research.