Incremental frequent itemsets mining based on frequent pattern tree and multi-scale

2021 ◽  
Vol 163 ◽  
pp. 113805
Author(s):  
Yaling Xun ◽  
Xiaohui Cui ◽  
Jifu Zhang ◽  
Qingxia Yin
Keyword(s):  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Multi Scale ◽  
Frequent Pattern Tree ◽  
Frequent Itemsets Mining

Research into the Algorithm of Frequent Pattern Mining Based on across Linker

2012 ◽  
Vol 195-196 ◽  
pp. 984-986
Author(s):  
Ming Ru Zhao ◽  
Yuan Sun ◽  
Jian Guo ◽  
Ping Ping Dong
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Important Data ◽  
Classical Algorithm ◽  
Frequent Itemsets Mining ◽  
Mining Frequent Itemsets

Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

scholarly journals An Extended Frequent Pattern Tree for Hiding Sensitive Frequent Itemsets

2011 ◽  
Vol 18D (3) ◽  
pp. 169-178
Author(s):  
Dan-Young Lee ◽  
Hyoung-Geun An ◽  
Jae-Jin Koh
Keyword(s):  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Frequent Pattern Tree

An UBMFFP Tree for Mining Multiple Fuzzy Frequent Itemsets

2015 ◽  
Vol 23 (06) ◽  
pp. 861-879 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Tzung-Pei Hong ◽  
Tsung-Ching Lin ◽  
Shing-Tai Pan
Keyword(s):  
Upper Bound ◽  
Frequent Itemsets ◽  
Tree Structure ◽  
Frequent Pattern ◽  
Second Phase ◽  
Two Phase ◽  
Tree Algorithm ◽  
Large Databases ◽  
Frequent Pattern Tree ◽  
Mining Algorithms

Frequent itemsets are useful for discovering interesting associations hidden in large databases. Many mining algorithms use data with binary attributes to represent the occurrence of items and find frequent itemsets. However, many real-world applications provide a richer source of transactions with quantitative values. The fuzzy frequent pattern tree algorithm was thus proposed for extracting fuzzy frequent itemsets from the quantitative transactions. In this paper, a tree structure called the upper-bound multiple fuzzy frequent-pattern (UBMFFP)-tree is designed for improving the pruning effect in the mining process. A two-phase fuzzy mining approach based on the tree structure is also proposed to obtain the complete fuzzy frequent itemsets from a quantitative database. The proposed fuzzy mining approach recursively and efficiently finds the upper-bound fuzzy counts of itemsets with the aid of the tree structure. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results indicate that the proposed UBMFFP-tree algorithm has good performance in terms of execution time and number of tree nodes.

scholarly journals Application of Frequent Itemsets Mining to Analyze Patterns of One-Stop Visits in Taiwan

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e14824 ◽  
Author(s):  
Chun-Yi Tu ◽  
Tzeng-Ji Chen ◽  
Li-Fang Chou
Keyword(s):  
Frequent Itemsets ◽  
One Stop ◽  
Frequent Itemsets Mining

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset 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
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