@article{(Open Science Index):https://publications.waset.org/pdf/13363,
	  title     = {An Efficient Data Mining Approach on Compressed Transactions},
	  author    = {Jia-Yu Dai and  Don-Lin Yang and  Jungpin Wu and  Ming-Chuan Hung},
	  country	= {},
	  institution	= {},
	  abstract     = {In an era of knowledge explosion, the growth of data
increases rapidly day by day. Since data storage is a limited resource,
how to reduce the data space in the process becomes a challenge issue.
Data compression provides a good solution which can lower the
required space. Data mining has many useful applications in recent
years because it can help users discover interesting knowledge in large
databases. However, existing compression algorithms are not
appropriate for data mining. In [1, 2], two different approaches were
proposed to compress databases and then perform the data mining
process. However, they all lack the ability to decompress the data to
their original state and improve the data mining performance. In this
research a new approach called Mining Merged Transactions with the
Quantification Table (M2TQT) was proposed to solve these problems.
M2TQT uses the relationship of transactions to merge related
transactions and builds a quantification table to prune the candidate
itemsets which are impossible to become frequent in order to improve
the performance of mining association rules. The experiments show
that M2TQT performs better than existing approaches.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {4},
	  year      = {2008},
	  pages     = {1035 - 1042},
	  ee        = {https://publications.waset.org/pdf/13363},
	  url   	= {https://publications.waset.org/vol/16},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 16, 2008},
	}