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Mining The Top-K Frequent Itemset With Minimum Length M (2001)
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BibTeX
@MISC{Cong01miningthe,
author = {Shi Cong and Simon Fraser University and Name Shi Cong},
title = {Mining The Top-K Frequent Itemset With Minimum Length M},
year = {2001}
}
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Abstract
With the explosive growth of data stored in electronic form, data mining has become essential in searching nontrivial, implicit, previously unknown and potentially useful information from a huge amount of data. Association rule mining in large transactional databases is an important topic in the field of data mining. It takes a set of transactions, which is a list of items, as input to find sets of objects tending to associate with each other. Having been investigated intensively during the past few years, it has been shown that the major computational task is to identify all of the frequent itemsets which satisfy a minimum support threshold, min-sup. Association rules can then be generated easily. In this work, I propose two interesting frequent itemset mining algorithms, DIPT and FIPT, which find the top-k frequent itemsets with the minimum length m in the transaction database. The novelty of these algorithms is that there is no need to specify a min-sup threshold by users. I compared these methods with other frequent itemset mining algorithms and showed that this approach presents greater advantages in terms of both flexibility and efficiency. Key words: data mining, association rule mining, frequent itemset mining iv Acknowledgments I would like to thank my senior supervisor, Dr. Jiawei Han, for all the support he has given me since I entered this graduate program. This thesis would not exist without his guidance and assistance. My gratitude also goes to my committee members, Dr. Ke Wang and Dr. Ramesh Krishnamurti, for their insightful comments and suggestions. I would like to take this opportunity to say thanks to everyone in the Intelligent Database Systems Research Laboratory of School of Computing Science at Simon Fraser University. Speci...
Keyphrases
minimum length data mining top-k frequent itemset association rule mining top-k frequent itemsets simon fraser university computing science useful information insightful comment electronic form senior supervisor committee member past year frequent itemsets interesting frequent itemset mining algorithm frequent itemset mining algorithm major computational task graduate program huge amount important topic large transactional database intelligent database system research laboratory explosive growth min-sup threshold ramesh krishnamurti minimum support threshold ke wang key word transaction database frequent itemset mining iv acknowledgment association rule jiawei han
