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Novelty as a Measure of Interestingness in Knowledge Discovery

By Vasudha Bhatnagar, Ahmed Sultan Al-hegami and Naveen Kumar

Abstract

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

Topics: Interestingness, Subjective Measures, Novelty Index
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.125.1365
Provided by: CiteSeerX
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