An Indexed Trie Approach to Incremental Mining of Closed Frequent Itemsets Based on a Galois Lattice Framework

2008 ◽  
pp. 486-495
Author(s):  
B. Kalpana ◽  
R. Nadarajan ◽  
J. Senthil Babu
Keyword(s):  
Frequent Itemsets ◽  
Incremental Mining ◽  
Galois Lattice ◽  
Closed Frequent Itemsets

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

2019 ◽  
Vol 892 ◽  
pp. 157-167
Author(s):  
Fatimah Audah Md Zaki ◽  
Nurul Fariza Zulkurnain
Keyword(s):  
Frequent Itemsets ◽  
Suitable Method ◽  
Closed Frequent Itemsets ◽  
Closed Itemsets ◽  
Synthetic Datasets

The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method.

Closed-Itemset Incremental-Mining Problem

2011 ◽  
pp. 150-153
Author(s):  
Luminita Dumitriu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Incremental Mining ◽  
Minimum Support ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree

2016 ◽  
Vol 24 (03) ◽  
pp. 367-386 ◽  
Author(s):  
Weigang Huo ◽  
Xingjie Feng ◽  
Zhiyuan Zhang
Keyword(s):  
Execution Time ◽  
Frequent Itemsets ◽  
Incremental Mining ◽  
Minimum Support ◽  
Efficient Approach ◽  
Support Threshold ◽  
Dynamic Databases ◽  
Norm Operator ◽  
Memory Cost

Keeping the generated fuzzy frequent itemsets up-to-date and discovering the new fuzzy frequent itemsets are challenging problems in dynamic databases. In this paper, the classical H-struct structure is extended to mining fuzzy frequent itemsets. The extended H-mine algorithm can use any t-norm operator to calculate the support of fuzzy itemset. The FP-tree-based structure called the Initial-FP-tree and the New-FP-tree are built to maintain the fuzzy frequent itemsets in the original database and the new inserted transactions respectively. The strategy of incremental mining of fuzzy frequent itemsets is achieved by breath-first-traversing the Initial-FP-tree and the New-FP-tree. All of the fuzzy frequent itemsets in the updated database can be obtained by traversing the Initial-FP-tree. The experiments on real datasets show that the proposed approach runs faster than the batch extended H-mine algorithm. Comparing with the existing algorithm for incremental mining fuzzy frequent itemsets, the proposed approach is superior in terms of the execution time. The memory cost of the proposed approach is lower than that of the existing algorithm when the minimum support threshold is low.

Sign in / Sign up

Export Citation Format

Share Document