Abstract: Rule Discovery is an important technique for mining
knowledge from large databases. Use of objective measures for
discovering interesting rules leads to another data mining problem,
although of reduced complexity. Data mining researchers have
studied subjective measures of interestingness to reduce the volume
of discovered rules to ultimately improve the overall efficiency of
KDD process.
In this paper we study novelty of the discovered rules as a
subjective measure of interestingness. We propose a hybrid approach
based on both objective and subjective measures to quantify novelty
of the discovered rules in terms of their deviations from the known
rules (knowledge). We analyze the types of deviation that can arise
between two rules and categorize the discovered rules according to
the user specified threshold. We implement the proposed framework
and experiment with some public datasets. The experimental results
are promising.
Abstract: This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.
Abstract: Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach that uses objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules. We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are quite promising.