@article{(Open Science Index):https://publications.waset.org/pdf/6970,
	  title     = {A Hybrid Approach for Quantification of Novelty in Rule Discovery },
	  author    = {Vasudha Bhatnagar and  Ahmed Sultan Al-Hegami and  Naveen Kumar},
	  country	= {},
	  institution	= {},
	  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.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {4},
	  year      = {2007},
	  pages     = {1003 - 1006},
	  ee        = {https://publications.waset.org/pdf/6970},
	  url   	= {https://publications.waset.org/vol/4},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 4, 2007},
	}