Mining Maximal Frequent Itemsets over Sampling Databases
Available Online January 2016.
- https://doi.org/10.2991/ifeea-15.2016.6How to use a DOI?
- Maximal Frequent Itemset, Sampling, Data Mining.
- Maximal frequent itemset is an important representation of frequent itemset. This paper focuses on how to achieve the maximal frequent itemsets over databases by sampling technique. We use a tree-based data synopsis to maintain the frequent itemsets, based on which, an efficient algorithm SMFI is proposed. Our extensive experimental studies over a dataset show that sampling is effective when mining the maximal frequent itemsets.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Haifeng Li PY - 2016/01 DA - 2016/01 TI - Mining Maximal Frequent Itemsets over Sampling Databases BT - 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015) PB - Atlantis Press SP - 28 EP - 31 SN - 2352-5401 UR - https://doi.org/10.2991/ifeea-15.2016.6 DO - https://doi.org/10.2991/ifeea-15.2016.6 ID - Li2016/01 ER -