Mining Infrequent Items using the Constructs of General and Enhanced Apriori Algorithms

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-25 Number-2
Year of Publication : 2015
Authors : Sudhir Tirumalasetty, Sreenivasa Reddy Edara
  10.14445/22312803/IJCTT-V25P119

MLA

Sudhir Tirumalasetty, Sreenivasa Reddy Edara "Mining Infrequent Items using the Constructs of General and Enhanced Apriori Algorithms". International Journal of Computer Trends and Technology (IJCTT) V25(2):101-105, July 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Discovering knowledge has extensively increased its applications in numerous areas. This knowledge can be discovered by various data mining techniques which provide solutions for countless number of problems. Association Rule Mining is one among the data mining techniques which is essential for mining frequent items. This technique helps in taking decisions in a decision making system like medicine, marketing, finance, fraud detection, etc. As discovering frequent items are important in a decision making systems for taking further decisions, mining infrequent items also plays pivotal role in a decision making system. Infrequent items reveal about the margins of decisions making systems. These margins caution a decision making when the system is going abnormal. In this paper, two methods based on the constructs of general Apriori algorithm and an improved version of Apriori algorithm is proposed for discovering infrequent items. Also another method is proposed, an enhanced version of the two proposed methods for discovering infrequent items. These proposed methods use negative frequency of items, negative support of items and complement of transactional data set.

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Keywords
Apriori, Complement Transactional Data Set, Infrequent items, Negative Frequency, Negative Support, Transactional Data Set.