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Understanding 9/11Blended intelligence of FCA with FLC for knowledge representation from clustered data in medical analysis
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Formal concept analysis is the process of data analysis mechanism with
emergent attractiveness across various fields such as data mining, robotics,
medical, big data and so on. FCA is helpful to generate the new learning
ontology based techniques. In medical field, some growing kids are facing
the problem of representing their knowledge from their gathered prior data
which is in the form of unordered and insufficient clustered data which is not
supporting them to take the right decision on right time for solving the
uncertainty based questionnaires. In the approach of decision theory, many
mathematical replicas such as probability-allocation, crisp set, and fuzzy
based set theory were designed to deals with knowledge representation based
difficulties along with their characteristic. This paper is proposing new
ideological blended approach of FCA with FLC and described with major
objectives: primarily the FCA analyzes the data based on relationships
between the set of objects of prior-attributes and the set of attributes based
prior-data, which the data is framed with data-units implicated composition
which are formal statements of idea of human thinking with conversion of
significant intelligible explanation. Suitable rules are generated to explore the
relationship among the attributes and used the formal concept analysis from
these suitable rules to explore better knowledge and most important factors
affecting the decision making. Secondly how the FLC derive the
fuzzification, rule-construction and defuzzification methods implicated for
representing the accurate knowledge for uncertainty based questionnaires.
Here the FCA is projected to expand the FCA based conception with help of
the objective based item set notions considered as the target which is
implicated with the expanded cardinalities along with its weights which is
associated through the fuzzy based inference decision rules. This approach is
more helpful for medical experts for knowing the range of patient’s memory
deficiency also for people whose are facing knowledge explorer deficiency.
emergent attractiveness across various fields such as data mining, robotics,
medical, big data and so on. FCA is helpful to generate the new learning
ontology based techniques. In medical field, some growing kids are facing
the problem of representing their knowledge from their gathered prior data
which is in the form of unordered and insufficient clustered data which is not
supporting them to take the right decision on right time for solving the
uncertainty based questionnaires. In the approach of decision theory, many
mathematical replicas such as probability-allocation, crisp set, and fuzzy
based set theory were designed to deals with knowledge representation based
difficulties along with their characteristic. This paper is proposing new
ideological blended approach of FCA with FLC and described with major
objectives: primarily the FCA analyzes the data based on relationships
between the set of objects of prior-attributes and the set of attributes based
prior-data, which the data is framed with data-units implicated composition
which are formal statements of idea of human thinking with conversion of
significant intelligible explanation. Suitable rules are generated to explore the
relationship among the attributes and used the formal concept analysis from
these suitable rules to explore better knowledge and most important factors
affecting the decision making. Secondly how the FLC derive the
fuzzification, rule-construction and defuzzification methods implicated for
representing the accurate knowledge for uncertainty based questionnaires.
Here the FCA is projected to expand the FCA based conception with help of
the objective based item set notions considered as the target which is
implicated with the expanded cardinalities along with its weights which is
associated through the fuzzy based inference decision rules. This approach is
more helpful for medical experts for knowing the range of patient’s memory
deficiency also for people whose are facing knowledge explorer deficiency.
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