Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
Many alternatives have been proposed for the mining of association rules involving rare but 'interesting' itemsets in a dataset where there also exist highly frequent itemsets. Nevertheless, all the approaches thus far suggested that we knew which those interesting itemsets are, as well as which is the right support value for them. None of the approaches proposed a way of automatically discovering such items. In this work we introduce the notion of locally frequent itemsets and support their existence as the biggest and most frequently appearing category of rare but interesting itemsets especially at commercial applications, based on the opinion of field experts. Subsequently we propose two algorithms for finding and handling these itemsets. The main idea is to divide the database into partitions according to the problem needs and besides searching for itemsets which are frequent in the whole database to search also for itemsets which are frequent if considered within these partitions. Our approach proves very effective and also very efficient as compared to the traditional algorithms both in synthetic and real data.
Intelligent Data Analysis – IOS Press
Published: Jan 1, 2005
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.