Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications.

View/ Open
PhD_Thesis_Yasmina-Bashon_2013-06-18.pdf (3.178Mb)
Download
Publication date
2014-05-02Author
Bashon, Yasmina M.Supervisor
Neagu, DanielRidley, Mick J.
Keyword
Similarity MeasuresFuzzy Geometrical Similarity Model
Fuzzy Set-Theoretical Similarity Model
Fuzzy objects
Heterogeneous data
Fuzzy attributes
Numerical attributes
Categorical attributes
Data objects
Comparison
Rights

The University of Bradford theses are licenced under a Creative Commons Licence.
Institution
University of BradfordDepartment
Department of Computing, School of Computing, Informatics and MediaAwarded
2013
Metadata
Show full item recordAbstract
This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.Type
ThesisQualification name
PhDCollections
Related items
Showing items related by title, author, creator and subject.
-
A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitationKabir, Sohag; Goek, T.K.; Kumar, M.; Yazdi, M.; Hossain, F. (2020)Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.
-
Predicting changing pattern: building model for consumer decision making in digital marketKumar, A.; Mangla, S.K.; Luthra, S.; Rana, Nripendra P.; Dwivedi, Y.K. (2018-09)Consumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context. Design/methodology/approach: To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data. Findings: The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal. Practical implications: The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk. Originality/value: This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era.
-
Intelligent condition monitoring using fuzzy inductive learningPeng, Yonghong (2004)