FCA-based Approach Research Articles

Data complexity: An FCA-based approach

In this paper we propose different indices for measuring the complexity of a dataset in terms of Formal Concept Analysis (FCA). We extend the lines of the research about the “closure structure” and the “closure index” based on minimum generators of intents (aka closed itemsets). We would try to capture statistical properties of a dataset, not just extremal characteristics, such as the size of a passkey. For doing so we introduce an alternative approach where we measure the complexity of a dataset w.r.t. five significant elements that can be computed in a concept lattice, namely intents (closed sets of attributes), pseudo-intents, proper premises, keys (minimal generators), and passkeys (minimum generators). Then we define several original indices allowing us to estimate the complexity of a dataset. Moreover we study the distribution of all these different elements and indices in various real-world and synthetic datasets. Finally, we investigate the relations existing between these significant elements and indices, and as well the relations with implications and association rules.

FCA-based approach for modeling the domain knowledge in an education system

In multidisciplinary courses, learners have diverse educational background and training on varying subjects. Subjects that such learners have studied may or may not relate to those of a course now being pursued. A requirement, therefore, arises to ascertain the level of understanding that the learners’ have acquired respectively in their chosen subjects. This paper proposes a FCA-based teaching methodology for the learners on the basis of their background knowledge. In our approach, we first present a mathematical model to calculate the learners’ prior knowledge on the subjects of pursuing course based on the learners’ background knowledge. Using the calculated prior knowledge on the pursuing subjects, the learners are then clustered by generating proto-fuzzy to deliver appropriate learning material to each of the learners. Finally, we discuss the proposed model with an example.

FCA-based approach for modeling the domain knowledge in an education system

FCA-based approach for modeling the domain knowledge in an education system

Introduction to formal concept analysis and its applications in reliability engineering

Formal Analysis of Concepts (FCA) is a method of data analysis that helps to study the relationship between a set of objects and a set of attributes (the formal context). FCA not only allows detecting data groups (concepts) and their graphical visualization, but also extracting rules that could reveal the underlying structure of the analyzed context. The main idea of this paper is to present the fundamentals of FCA and how it can be used in reliability engineering problems. To this aim, examples in reliability engineering, from both the literature and authors’ experience, have been selected for analysis. Comments on the new insights provided by FCA are also highlighted. Finally, the results from the examples selected show that other reliability areas could benefit from using an FCA-based approach.

Minimal implications base for social network analysis

PurposeCurrently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and completely observed. Therefore, it is relevant to discover substructures and potential behavior patterns in SN. Recently, formal concept analysis (FCA) has been applied for this purpose. FCA is a concept analysis theory that identifies concept structures within a data set. The representation of SN patterns through implication rules based on FCA enables the identification of relevant substructures that cannot be easily identified. The authors’ approach considers a minimum and irreducible set of implication rules (stem base) to represent the complete set of data (activity in the network). Applying this to an SN is of interest because it can represent all the relationships using a reduced form. So, the purpose of this paper is to represent social networks through the steam base.Design/methodology/approachThe authors’ approach permits to analyze two-mode networks by transforming access activities of SN into a formal context. From this context, it can be extracted to a minimal set of implications applying the NextClosure algorithm, which is based on the closed sets theory that provides to extract a complete, minimal and non-redundant set of implications. Based on the minimal set, the authors analyzed the relationships between premises and their respective conclusions to find basic user behaviors.FindingsThe experiments pointed out that implications, represented as a complex network, enable the identification and visualization of minimal substructures, which could not be found in two-mode network representation. The results also indicated that relations among premises and conclusions represent navigation behavior of SN functionalities. This approach enables to analyze the following behaviors: conservative, transitive, main functionalities and access time. The results also demonstrated that the relations between premises and conclusions represented the navigation behavior based on the functionalities of SN. The authors applied their approach for an SN for a relationship to explore the minimal access patterns of navigation.Originality/valueThe authors present an FCA-based approach to obtain the minimal set of implications capable of representing the minimum structure of the users’ behavior in an SN. The paper defines and analyzes three types of rules that form the sets of implications. These types of rules define substructures of the network, the capacity of generation users’ behaviors, transitive behavior and conservative capacity when the temporal aspect is considered.

Reusing process fragments for fast service composition: a clustering-based approach

ABSTRACTWith the proliferation of Web services over the Internet and due to the increasing complexity of users’ needs, Web service composition has emerged as a powerful method of software reuse, allowing to deliver complete business processes as a set of interacting services. To guarantee a rapid and secure service composition, fragments of available business processes at different granularities may be considered as a composition unit and recombined to deliver effective compositions. Despite the benefits of this method, most of the existing works do not take into consideration the reuse of service process fragments (SPF). Reusing SPFs allows, not only to minimize the composition time, but also to improve the reliability of the composition process. In this paper, we propose a Web service composition approach that aims to combine service process fragments rather that atomic services. We adopt a powerful mathematical model called Formal Concept Analysis (FCA) to define the relationships between services and fragments. Moreover, we exploit the regrouping capabilities of FCA by proposing algorithms for the extraction of candidate fragments’ combinations. A scoring function is also defined to determine the quality level of each SPF and its ability to participate in a composition. The experimental studies proved the effectiveness of our FCA-based approach compared to existing state-of-the-art solutions.

Measuring Spatial Accessibility to Services within Indices of Multiple Deprivation: Implications of Applying an Enhanced two-Step Floating Catchment Area (E2SFCA) Approach

Approaches to calculating spatial accessibility within existing indices of multiple deprivation (IMD) methodologies are based on ‘traditional’ accessibility metrics and tend not to adopt more recent methodological enhancements. In particular, the last decade has seen a relatively large body of studies that have applied floating catchment area (FCA) methods that account for both service supply and potential demand interactions, mediated by the impact of distance, in a wide range of application areas. In this paper, we investigate potential implications of incorporating an FCA-based approach to measuring spatial accessibility within an existing IMD framework. Using the Welsh Index of Multiple Deprivation (WIMD) as a case study, FCA-derived accessibility scores were substituted for the existing approach used to calculate accessibility and a revised index was computed. The published methodologies used to construct the other ‘domains’ within the WIMD were followed and the implications for the overall deprivation measure were assessed. Statistical and visualisation tools revealed implications for both the access and overall IMD rankings, with sparsely populated (predominantly rural) areas tending to receive higher accessibility scores from FCA-based approaches than more densely populated (predominantly urban) areas. These areas in turn showed the greatest decline in ranking on the WIMD calculations following the application of FCA approaches. Potential reasons for such trends are posited before we conclude by drawing attention to the implications of adopting FCA-based approaches to calculate IMDs particularly for those policies designed to distribute funds or allocate resources to areas of need.

A step forward for Topic Detection in Twitter: An FCA-based approach

The Topic Detection Task in Twitter represents an indispensable step in the analysis of text corpora and their later application in Online Reputation Management. Classification, clustering and probabilistic techniques have been traditionally applied, but they have some well-known drawbacks such as the need to fix the number of topics to be detected or the problem of how to integrate the prior knowledge of topics with the detection of new ones. This motivates the current work, where we present a novel approach based on Formal Concept Analysis (FCA), a fully unsupervised methodology to group similar content together in thematically-based topics (i.e., the FCA formal concepts) and to organize them in the form of a concept lattice. Formal concepts are conceptual representations based on the relationships between tweet terms and the tweets that have given rise to them. It allows, in contrast to other approaches in the literature, their clear interpretability. In addition, the concept lattice represents a formalism that describes the data, explores correlations, similarities, anomalies and inconsistencies better than other representations such as clustering models or graph-based representations. Our rationale is that these theoretical advantages may improve the Topic Detection process, making them able to tackle the problems related to the task. To prove this point, our FCA-based proposal is evaluated in the context of a real-life Topic Detection task provided by the Replab 2013 CLEF Campaign. To demonstrate the efficiency of the proposal, we have carried out several experiments focused on testing: (a) the impact of terminology selection as an input to our algorithm, (b) the impact of concept selection as the outcome of our algorithm, and; (c) the efficiency of the proposal to detect new and previously unseen topics (i.e., topic adaptation). An extensive analysis of the results has been carried out, proving the suitability of our proposal to integrate previous knowledge of prior topics without losing the ability to detect novel and unseen topics as well as improving the best Replab 2013 results.

Using Ontologies and Formal Concept Analysis to Integrate Heterogeneous Tourism Information

This paper presents an ontology-based approach and a formal concept analysis (FCA) approach to integrating heterogeneous tourism information for online tour planning. Two ontologies, an ontology for tourists and an ontology for tourism information providers, are developed with respect to their own perspective. The ontology for tourists is developed from the tourism literature. The ontology for tourism information providers is developed by integrating heterogeneous online tourism information using a FCA approach. The two ontologies are then mapped using the FCA and Bayesian analysis to evaluate tourists' preferences against information published by tourism information providers. An example of planning a tour to New York City is used to illustrate the proposed ontology approach. An analytic hierarchy process is used to rank the tourism attractions suggested by the ontology and FCA-based approaches.

Efficient synthesis of feature models

ContextVariability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, creating feature models from large, legacy systems is a long and arduous task. We describe three synthesis scenarios that can benefit from the algorithms in this paper. ObjectiveThis paper addresses the problem of automatic synthesis of feature models from propositional constraints. We show that the decision version of the problem is NP-hard. We designed two efficient algorithms for synthesis of feature models from CNF and DNF formulas respectively. MethodWe performed an experimental evaluation of the algorithms against a binary decision diagram (BDD)-based approach and a formal concept analysis (FCA)-based approach using models derived from realistic models. ResultsOur evaluation shows a 10 to 1,000-fold performance improvement for our algorithms over the BDD-based approach. The performance of the DNF-based algorithm was similar to the FCA-based approach, with advantages for both techniques. We identified input properties that affect the runtimes of the CNF- and DNF-based algorithms. ConclusionsOur algorithms are the first known techniques that are efficient enough to be used on dependencies extracted from real systems, opening new possibilities of creating reverse engineering and model management tools for variability models.

Auditing the Semantic Completeness of SNOMED CT Using Formal Concept Analysis

ObjectiveThis study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)–based model. DesignWe developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation. MeasurementsIn Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding). ResultsA total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT. ConclusionThe anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.