Mining Statistically Important Equivalence Classes

author: Jinyan Li, Institute for Infocomm Research
published: Aug. 14, 2007,   recorded: August 2007,   views: 3940
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

The support condence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that efectively simplifies the search lattice. This computational convenience brings both quality and statistical laws to the results as observed by many previous studies. In this paper, we introduce a novel algorithm that produces itemsets with ranked statistical merits under sophisticated test statistics such as chi-square, risk ratio, odds ratio, etc. Our algorithm is based on the concept of equivalence classes. An equivalence class is a set of frequent itemsets that always occur together in the same set of transactions. Therefore, itemsets within an equivalence class all share the same level of statistical signifiance regardless of the variety of test statistics. As an equivalence class can be uniquely determined and concisely represented by a closed pattern and a set of generators, we just mine closed patterns and generators, taking a simultaneous depth first search scheme. This parallel approach has not been exploited by any prior work. We evaluate our algorithm on two aspects. In general, we compare to LCM and FPclose which are the best algorithms tailored for mining only closed patterns. In particular, we compare to epMiner which is the most recent algorithm for mining a type of relative risk patterns, known as minimal emerging patterns. Experimental results show that our algorithm is faster than all of them, sometimes even multiple orders of magnitude faster. These statistically ranked patterns and the eficiency have a high potential for real life applications, especially in biomedical and nancial fields where classical test statistics are of dominant interest.

See Also:

Download slides icon Download slides: kdd07_li_msie_01.ppt (625.5┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: