Mining Interesting Rare Items with Maximum Constraint Model Based on Tree Structure
- Resource Type
- Conference
- Authors
- Bhatt, Urvi Y.; Patel, Pratik A.
- Source
- 2015 Fifth International Conference on Communication Systems and Network Technologies Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on. :1065-1070 Apr, 2015
- Subject
- Communication, Networking and Broadcast Technologies
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Itemsets
Association rules
Algorithm design and analysis
Generators
Buildings
Rare Pattern Mining
Frequent Pattern
FP-Growth
Maximum Item Support Constraint
- Language
Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent item sets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items.