Discovering Chronic-Frequent Patterns in Transactional Databases

This paper investigates the partial periodic behavior of the frequent patterns in a transactional database, and introduces a new class of user-interest-based patterns known as chronic-frequent patterns. Informally, a frequent pattern is said to be chronic if it has sufficient number of cyclic repetitions in a database. The proposed patterns can provide useful information to the users in many real-life applications. An example is finding chronic diseases in a medical database. The chronic-frequent patterns satisfy the anti-monotonic property. This property makes the pattern mining practicable in real-world applications. The existing pattern growth techniques that are meant to discover frequent patterns cannot be used for finding the chronic-frequent patterns. The reason is that the tree structure employed by these techniques’ capture only the frequency and disregards the periodic behavior of the patterns. We introduce another pattern-growth algorithm which employs an alternative tree structure, called Chronic-Frequent pattern tree (CFP-tree), to capture both frequency and periodic behavior of the patterns. Experimental results show that the proposed patterns can provide useful information and our algorithm is efficient.

[1]  R V Pollock.  Advances in information technology. , 2001, The Veterinary clinics of North America. Equine practice.

[2]  Hong Chen,et al.  An Efficient Algorithm for Frequent Itemset Mining on Data Streams , 2006, Industrial Conference on Data Mining.

[3]  Shih-Sheng Chen,et al.  New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports , 2011, J. Syst. Softw..

[4]  Masaru Kitsuregawa,et al.  Discovering Quasi-Periodic-Frequent Patterns in Transactional Databases , 2013, BDA.

[5]  Philippe Lenca,et al.  Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold , 2009, IAIT.

[6]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[7]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[8]  P. Krishna Reddy,et al.  Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases , 2016, DEXA.

[9]  P. Krishna Reddy,et al.  An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases , 2011, PAKDD Workshops.

[10]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[11]  Philip S. Yu,et al.  InfoMiner+: mining partial periodic patterns with gap penalties , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[12]  Walid G. Aref,et al.  Incremental, online, and merge mining of partial periodic patterns in time-series databases , 2004, IEEE Transactions on Knowledge and Data Engineering.

[13]  James Bailey,et al.  New Frontiers in Applied Data Mining , 2011, Lecture Notes in Computer Science.

[14]  Jiawei Han,et al.  Mining Segment-Wise Periodic Patterns in Time-Related Databases , 1998, KDD.

[15]  Nikos Mamoulis,et al.  Discovering Partial Periodic Patterns in Discrete Data Sequences , 2004, PAKDD.

[16]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[17]  Ron Kohavi,et al.  Real world performance of association rule algorithms , 2001, KDD '01.

[18]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[19]  Cláudia Antunes,et al.  Temporal Data Mining: an overview , 2001 .

[20]  Petra Perner,et al.  Advances in Data Mining , 2002, Lecture Notes in Computer Science.

[21]  Sridhar Ramaswamy,et al.  Cyclic association rules , 1998, Proceedings 14th International Conference on Data Engineering.

[22]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[23]  P. Krishna Reddy,et al.  An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns , 2011, DASFAA.

[24]  Young-Koo Lee,et al.  Discovering Periodic-Frequent Patterns in Transactional Databases , 2009, PAKDD.

[25]  Walid G. Aref,et al.  On the Discovery of Weak Periodicities in Large Time Series , 2002, PKDD.