A false negative approach to mining frequent itemsets from high speed transactional data streams

False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams

An Efficient Incremental Algorithm to Mine Closed Frequent Itemsets over Data Streams

The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for Incremental Mining of Closed Frequent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table Item...

Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window

Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summar...

DELAY-CFIM: A Sliding Window Based Method on Mining Closed Frequent Itemsets over High-Speed Data Streams

Closed frequent itemset mining plays an essential role in data stream mining. It could be used in business decisions, basket analysis, etc. Most methods for mining closed frequent itemsets store the streamlined information in compact data structure when data is generated. Whenever a query is submitted, it outputs all closed frequent itemsets. However, the online processing of existing approache...

CLAIM: An Efficient Method for Relaxed Frequent Closed Itemsets Mining over Stream Data

Recently, frequent itemsets mining over data streams attracted much attention. However, mining closed itemsets from data stream has not been well addressed. The main difficulty lies in its high complexity of maintenance aroused by the exact model definition of closed itemsets and the dynamic changing of data streams. In data stream scenario, it is sufficient to mining only approximated frequent...