Novelty as a Measure of Interestingness in Knowledge Discovery
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 the authors study novelty of the discovered rules as a subjective measure of interestingness. They 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). They analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold.