Transactions on Large-Scale Data- and Knowledge-Centered Systems IV - Special Issue on Database Systems for Biomedical Applications

The support and periodicity are two important dimensions to determine the interestingness of a pattern in a dataset. Periodicfrequent patterns are an important class of regularities that exist in a dataset with respect to these two dimensions. Most previous models on periodic-frequent pattern mining have focused on finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. These models suffer from the following two obstacles: (i) Current periodic-frequent pattern models cannot handle datasets in which multiple transactions can share a common time stamp and/or transactions occur at irregular time intervals (ii) The usage of single minSup and maxPer for finding the patterns leads to the rare item problem. This paper tries to address these two obstacles by proposing a novel model to discover periodiccorrelated patterns in a temporal database. Considering the input data as a temporal database addresses the first obstacle, while finding periodiccorrelated patterns address the second obstacle. The proposed model employs all-confidence measure to prune the uninteresting patterns in support dimension. A new measure, called periodic-all-confidence, is being proposed to filter out uninteresting patterns in periodicity dimension. A pattern-growth algorithm has also been discussed to find periodiccorrelated patterns. Experimental results show that the proposed model is efficient.

[1]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

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

[3]  Jiawei Han,et al.  CoMine: efficient mining of correlated patterns , 2003, Third IEEE International Conference on Data Mining.

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

[5]  Yen-Liang Chen,et al.  Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism , 2004, Decision Support Systems.

[6]  Jiawei Han,et al.  CCMine: Efficient Mining of Confidence-Closed Correlated Patterns , 2004, PAKDD.

[7]  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).

[8]  Hiroki Arimura,et al.  LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining , 2005 .

[9]  Hui Xiong,et al.  Hyperclique pattern discovery , 2006, Data Mining and Knowledge Discovery.

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

[11]  P. Krishna Reddy,et al.  Selecting a Right Interestingness Measure for Rare Association Rules , 2010, COMAD.

[12]  Kevin Y. Yip,et al.  Mining periodic patterns with gap requirement from sequences , 2007 .

[13]  Ho-Jin Choi,et al.  Efficient Mining Regularly Frequent Patterns in Transactional Databases , 2012, DASFAA.

[14]  Zhaohui Wu,et al.  Mining Both Associated and Correlated Patterns , 2006, International Conference on Computational Science.

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

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

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

[18]  Sangkyum Kim,et al.  Efficient Mining of Top Correlated Patterns Based on Null-Invariant Measures , 2011, ECML/PKDD.

[19]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

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

[21]  P. Krishna Reddy,et al.  Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms , 2011, EDBT/ICDT '11.

[22]  Jiawei Han,et al.  Re-examination of interestingness measures in pattern mining: a unified framework , 2010, Data Mining and Knowledge Discovery.

[23]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[24]  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.

[25]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[26]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[27]  Philippe Lenca,et al.  A Clustering of Interestingness Measures , 2004, Discovery Science.

[28]  Philip S. Yu,et al.  Mining Asynchronous Periodic Patterns in Time Series Data , 2003, IEEE Trans. Knowl. Data Eng..

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

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

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

[32]  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).

[33]  Zhaohui Wu,et al.  Efficiently Mining Mutually and Positively Correlated Patterns , 2006, ADMA.