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A Contrast Pattern based Clustering Algorithm for Categorical DataFore, Neil Koberlein 13 October 2010 (has links)
No description available.
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A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributesBashon, Yasmina M., Neagu, Daniel, Ridley, Mick J. 09 1900 (has links)
No / Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex data. In this paper, a framework of new and unified similarity measures is proposed for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes. Examples are used to illustrate, compare and discuss the applications and efficiency of the proposed approach to heterogeneous data comparison and clustering.
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Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applicationsBashon, Yasmina Massoud January 2013 (has links)
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.
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Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications.Bashon, Yasmina M. January 2013 (has links)
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. / Libyan Embassy
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