—An efficient algorithm for mining important association rule from multi-relational database using distributed mining ideas. Most existing data mining approaches look for rules i...
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
We present D-HOTM, a framework for Distributed Higher Order Text Mining based on named entities extracted from textual data that are stored in distributed relational databases. Unl...
We have a large database consisting of sales transactions. We investigate the problem of online mining of association rules in this large database. We show how to preprocess the d...
This paper investigates a brute-force technique for mining classification rules from large data sets. We employ an association rule miner enhanced with new pruning strategies to c...