Results 111 to 120 of about 846,106 (226)

Mining Recent Frequent Itemsets in Sliding Windows over Data Streams [PDF]

, 2012
This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm
Han, Congying, He, Guoping, Xu, Lijun
core  

Fast Distributed Algorithm of Mining Global Frequent Itemsets

, 2011
Most distributed algorithms of mining global frequent itemsets worked on net structure network and adopted Apriori-like algorithm. Whereas there were some problems in these algorithms: a lot of candidate itemsets and heavy communication traffic.
Bo He
core   +1 more source

Moment: Maintaining closed frequent itemsets over a stream sliding window

, 2004
This paper considers the problem of mining closed frequent itemsets over a sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent ...
Philip S. Yu, Yun Chi, Richard R. Muntz, Haixun Wang   +3 more
core  

An efficient approach for interactive mining of frequent itemsets

, 2005
There have been many studies on efficient discovery of frequent itemsets in large databases. However, it is nontrivial to mine frequent itemsets under interactive circumstances where users often change minimum support threshold (minsup) because the ...
Xin Li, Deng, ZH, Li, X, Tang, SW, Tang, Shi-Wei, Deng, Zhi-Hong, Li, Xin, Zhi-Hong Deng, Shi-Wei Tang   +8 more
core   +1 more source

Efficiently mining frequent itemsets from very large databases [PDF]

, 2004
Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure ...
Zhu, Jianfei
core   +1 more source

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

IEEE Access
Frequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transactional
Muhammad Sadeequllah, Azhar Rauf, Saif Ur Rehman, Noha Alnazzawi   +3 more
doaj   +1 more source

Efficient Frequent Itemsets Mining by Sampling

, 2008
. As the first stage for discovering association rules, frequent itemsets mining is an important challenging task for large databases. Sampling provides an efficient way to get approximating answers in much shorter time.
Chengqi Zhang, Yanchang Zhao, Shichao Zhang   +2 more
core  

Distributed mining of frequent closed itemsets: some preliminary results

, 2005
In this paper we address the problem of mining frequent closed itemsets in a distributed setting. We gure out an environment where a transactional dataset is horizontally partitioned and stored in di erent sites.
Lucchese C, Perego R, Orlando S
core  

Ameliorated Algorithm to Maintain Discovered Frequent Itemsets

It is an important task in data mining to maintain discovered frequent itemsets for association rule mining. Because most time-consuming operation for mining association rules is to find the frequent itemsets from the transaction database.
Makinouchi, Akifumi, 杜, 孝平, Du, Xiaoping, ト, コウヘイ, 牧之内, 顕文, マキノウチ, アキフミ   +5 more
core  

Using Attribute Value Lattice to Find Closed Frequent Itemsets

, 2008
Finding all closed frequent itemsets is a key step of association rule mining since the non-redundant association rule can be inferred from all the closed frequent itemsets. In this paper we present a new method for finding closed frequent itemsets based
Eric Louie, T. Y. Lin Xiaohua, Tony Hu
core