FIA: Frequent Itemsets Mining Based on Approximate Counting in Data Streams

2009 ◽  
pp. 312-322
Author(s):  
Younghee Kim ◽  
Joonsuk Ryu ◽  
Ungmo Kim
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Approximate Counting ◽  
Frequent Itemsets Mining

Using hashing and lexicographic order for Frequent Itemsets Mining on data streams

2019 ◽  
Vol 125 ◽  
pp. 58-71 ◽  
Author(s):  
Lázaro Bustio-Martínez ◽  
Martín Letras-Luna ◽  
René Cumplido ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe ◽  
...  
Keyword(s):  
Data Streams ◽  
Lexicographic Order ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

scholarly journals New Algorithm for Frequent Itemsets Mining from Evidential Data Streams

2016 ◽  
Vol 96 ◽  
pp. 645-653 ◽  
Author(s):  
Amine Farhat ◽  
Mohamed Salah Gouider ◽  
Lamjed Ben Said
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining

scholarly journals Hardware Acceleration of Frequent Itemsets Mining on Data Streams

2014 ◽  
Vol 71 (1) ◽  
pp. 13-22
Author(s):  
Lázaro Bustio-Martínez ◽  
René Cumplido-Parra ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe
Keyword(s):  
Data Streams ◽  
Hardware Acceleration ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining

scholarly journals Mining Recent Maximal Frequent Itemsets Over Data Streams with Sliding Window

2018 ◽  
Vol 16 (6) ◽  
pp. 961-969 ◽  
Author(s):  
Saihua Cai ◽  
Shangbo Hao ◽  
Ruizhi Sun ◽  
Gang Wu
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Memory Usage ◽  
Time Cost ◽  
Improved Method ◽  
Huge Number ◽  
Frequent Itemsets Mining ◽  
Series Of Experiments ◽  
Maximal Frequent Itemsets

Abstract: The huge number of data streams makes it impossible to mine recent frequent itemsets. Due to the maximal frequent itemsets can perfectly imply all the frequent itemsets and the number is much smaller, therefore, the time cost and the memory usage for mining maximal frequent itemsets are much more efficient. This paper proposes an improved method called Recent Maximal Frequent Itemsets Mining (RMFIsM) to mine recent maximal frequent itemsets over data streams with sliding window. The RMFIsM method uses two matrixes to store the information of data streams, the first matrix stores the information of each transaction and the second one stores the frequent 1-itemsets. The frequent p-itemsets are mined with “extension” process of frequent 2-itemsets, and the maximal frequent itemsets are obtained by deleting the sub-itemsets of long frequent itemsets. Finally, the performance of the RMFIsM method is conducted by a series of experiments, the results show that the proposed RMFIsM method can mine recent maximal frequent itemsets efficiently

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