Incremental Mining of Closed Frequent Subtrees

2010 ◽  
pp. 356-370
Author(s):  
Viet Anh Nguyen ◽  
Akihiro Yamamoto
Keyword(s):  
Incremental Mining

IMIDB: An Algorithm for Indexed Mining of Incremental Databases

2017 ◽  
Vol 26 (1) ◽  
pp. 69-85
Author(s):  
Mohammed M. Fouad ◽  
Mostafa G.M. Mostafa ◽  
Abdulfattah S. Mashat ◽  
Tarek F. Gharib
Keyword(s):  
Data Structure ◽  
Association Rules ◽  
Efficient Algorithm ◽  
Performance Comparison ◽  
Incremental Mining ◽  
Itemset Mining ◽  
Large Databases ◽  
Transactional Databases ◽  
Dynamic Databases ◽  
Database Size

AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

Incremental mining of closed inter-transaction itemsets over data stream sliding windows

2011 ◽  
Vol 37 (2) ◽  
pp. 208-220 ◽  
Author(s):  
Shih-Chuan Chiu ◽  
Hua-Fu Li ◽  
Jiun-Long Huang ◽  
Hsin-Han You
Keyword(s):  
Data Stream ◽  
Incremental Mining ◽  
Sliding Windows

IMCS: Incremental Mining of Closed Sequential Patterns

2007 ◽  
pp. 50-61 ◽  
Author(s):  
Lei Chang ◽  
Dongqing Yang ◽  
Tengjiao Wang ◽  
Shiwei Tang
Keyword(s):  
Sequential Patterns ◽  
Incremental Mining

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.

Incremental Mining of Frequent Query Patterns from XML Queries for Caching

2006 ◽  
Author(s):  
Guoliang Li ◽  
Jianhua Feng ◽  
Jianyong Wang ◽  
Yong Zhang ◽  
Lizhu Zhou
Keyword(s):  
Incremental Mining ◽  
Query Patterns
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