A block-based approach for frequent itemset mining over data streams

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.

Download Full-text

Efficient Frequent Itemset Mining from Dense Data Streams

Web Technologies and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-11116-2_56 ◽

2014 ◽

pp. 593-601 ◽

Cited By ~ 5

Author(s):

Alfredo Cuzzocrea ◽

Fan Jiang ◽

Wookey Lee ◽

Carson K. Leung

Keyword(s):

Data Streams ◽

Frequent Itemset ◽

Frequent Itemset Mining ◽

Itemset Mining ◽

Dense Data

Download Full-text

Frequent Itemset Mining over Data Streams

Data-Centric Systems and Applications - Data Stream Management ◽

10.1007/978-3-540-28608-0_10 ◽

2016 ◽

pp. 209-219 ◽

Cited By ~ 1

Author(s):

Gurmeet Singh Manku

Keyword(s):

Data Streams ◽

Frequent Itemset ◽

Frequent Itemset Mining ◽

Itemset Mining

Download Full-text

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1666.029420 ◽

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.

Download Full-text