Automatic extraction of domain-specific stopwords from labeled documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Two measures of objective novelty in association rule mining
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
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The mining association algorithm is one of the most important data mining algorithms to derive association rules at high speed from huge databases. However, the algorithm tends to derive those rules that contain noises such as stopwords then some systems remove the noises using noise filters. We have been improving the algorithm and developing navigation systems for semi-structured data using the algorithm, and we also use a dictionary to remove noises from derived association rules. In order to derive effective rules, it is very important how to determine system parameters such as threshold values of the minimum support and the minimum confidence. Then we have adapted the ROC analysis to the algorithm on our navigation systems and evaluated the performance of derived rules. In this paper, we import the parameters from the ROC analysis into the algorithm to propose extended mining association algorithms. Moreover, we evaluate the performance of our proposed algorithms using an experimental database and show how our proposed algorithms can derive effective association rules. We also show that our proposed algorithms can remove stopwords automatically from raw data.