Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

Method to find both frequent closed itemsets and biclusters in high-dimensional binary data

During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner.

Bittable_TID is a biclustering tool written in MATLAB. It provides a fast solution for finding all biclusters within a binary data matrix.

It is also described in:
A Király, A. Gyenesei, J. Abonyi, Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data, The Scientific World Journal, vol. 2014, Article ID 870406, 7 pages

You can download other software tools used for comparison and data sets from here:
http://www.abonyilab.com/biclustering