A Frequent Item Graph Approach for Discovering Frequent Itemsets
Pages 952 - 956
Abstract
Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient approach for mining complete sets of frequent item sets. It is a modification of FP-tree. The contribution of this approach is to count the frequent 2-item sets and to form a graphical structure which extracts all possible frequent item sets in the database. We present performance comparisons for our algorithm against FP-growth algorithm.
- A Frequent Item Graph Approach for Discovering Frequent Itemsets
Recommendations
An Efficient Hash-Based Method for Discovering the Maximal Frequent Set
COMPSAC '01: Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software DevelopmentThe association rule mining can be divided into two steps. The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold. The second step is to generate reliable association rules based ...
An adaptive approach to mining frequent itemsets efficiently
The mining of frequent itemsets is a fundamental and important task of data mining. To improve the efficiency in mining frequent itemsets, many researchers developed smart data structures to represent the database, and designed divide-and-conquers ...
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
One of the most well-studied problems in data mining is computing the collection of frequent itemsets in large transactional databases. Since the introduction of the famous Apriori algorithm [14], many others have been proposed to find the frequent ...
Comments
Information & Contributors
Information
Published In
December 2008
1066 pages
ISBN:9780769534893
Publisher
IEEE Computer Society
United States
Publication History
Published: 20 December 2008
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Jan 2025