Novel techniques and an efficient algorithm for closed pattern mining

•Frequent closed itemset mining and biclustering can be reduced to the same problem.•A new and efficient algorithm for mining frequent closed patterns is presented.•We introduce a unique approach to transform {-1,0,1}-type data into binary format.•We propose an original aggregation method to detect...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications 2014-09, Vol.41 (11), p.5105-5114
Main Authors: Király, András, Laiho, Asta, Abonyi, János, Gyenesei, Attila
Format: Article
Language:English
Subjects:
Agglomeration
Algorithms
Applied sciences
Biclustering
Closed frequent itemset mining
Clustering visualization
Computer science
control theory
systems
Data mining
Data mining algorithm
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
Joining
Memory organisation. Data processing
Pattern analysis
Pattern detection
Software
Uniqueness
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Frequent closed itemset mining and biclustering can be reduced to the same problem.•A new and efficient algorithm for mining frequent closed patterns is presented.•We introduce a unique approach to transform {-1,0,1}-type data into binary format.•We propose an original aggregation method to detect the most meaningful patterns.•We offer a novel technique for visualization of biclustering results. In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0–1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.02.029