An Efficient Algorithm for Frequent Itemset Mining on Data Streams

2006 ◽  
pp. 474-491 ◽  
Author(s):  
Xie Zhi-jun ◽  
Chen Hong ◽  
Cuiping Li
Keyword(s):  
Data Streams ◽  
Efficient Algorithm ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining

Probabilistic frequent itemset mining over uncertain data streams

2018 ◽  
Vol 112 ◽  
pp. 274-287 ◽  
Author(s):  
Haifeng Li ◽  
Ning Zhang ◽  
Jianming Zhu ◽  
Yue Wang ◽  
Huaihu Cao
Keyword(s):  
Data Streams ◽  
Uncertain Data ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Uncertain Data Streams

An efficient algorithm for fuzzy frequent itemset mining

2020 ◽  
Vol 38 (5) ◽  
pp. 5787-5797
Author(s):  
Tsu-Yang Wu ◽  
Jerry Chun-Wei Lin ◽  
Unil Yun ◽  
Chun-Hao Chen ◽  
Gautam Srivastava ◽  
...  
Keyword(s):  
Efficient Algorithm ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining

scholarly journals TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data

2013 ◽  
Vol 10 (5) ◽  
pp. 1580-1586
Author(s):  
V.sidda Reddy ◽  
Dr T.V. Rao ◽  
Dr A. Govardhan
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Streaming Data ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Proposed Model ◽  
Mining Model ◽  
Mining Algorithms ◽  
Memory Efficient

Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these constraints is inversely proportional to the streaming speed of the data. Since the caching and mining the streaming-data is sensitive, here in this paper a scalable, memory efficient caching and frequent itemset mining model is devised. The proposed model is an incremental approach that builds single level multi node trees called bushes from each window of the streaming data; henceforth we refer this proposed algorithm as a Tree (bush) based Incremental Frequent Itemset Mining (TIFIM) over data streams.

Efficient Frequent Itemset Mining from Dense Data Streams

2014 ◽  
pp. 593-601 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Fan Jiang ◽  
Wookey Lee ◽  
Carson K. Leung
Keyword(s):  
Data Streams ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Dense Data

scholarly journals Reduction of Frequent Itemsets Mining in Big Data with the Help of FP Algorithm and Msegt-Tree

2020 ◽  
Vol 9 (4) ◽  
pp. 2169-2172
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Distributed Data ◽  
Root Node ◽  
Itemset Mining ◽  
Space And Time ◽  
Distributed Data Streams ◽  
The Cost

Frequent itemset mining is very crucial to minimize the cost and time of executions but when considering multiple distributed data streams in big data the frequent itemset mining has been a little cost consuming and taking more space and time complexity. In this paper we reduce the load and minimize the cost while minimizing the space and time complexities of the process by using reduction mechanism and indexing structures for preserving complexities. A 2-level architecture modal which will be helpful in handling the distributed data streams where the root node will be in level-0 and local nodes at level-1 is proposed. Each local node will evaluate the patterns in their specific data stream using the algorithm ‘FP’ which will help in lessening the burden on the root node and will be sent to root. With help of the patterns received from local nodes the root will generate a global pattern set.

An Efficient Frequent Itemset Mining Method over High-speed Data Streams

2012 ◽  
Vol 55 (11) ◽  
pp. 1357-1366 ◽  
Author(s):  
M. Memar ◽  
M. Deypir ◽  
M. H. Sadreddini ◽  
S. M. Fakhrahmad
Keyword(s):  
Data Streams ◽  
High Speed ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Mining Method ◽  
Itemset Mining ◽  
High Speed Data
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