A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams

The problem of mining frequent patterns in a static database is studied extensively in the literature by many researchers. Conventional frequent pattern algorithms are not applicable to find frequent patterns from the temporal database. Temporal database is a database which can store past, present and future information. A temporal relation may be viewed as a database of time invariant and time variant relation instances. The objective of this research is to come up with a novel approach so as to find the temporal association patterns similar to a given reference support sequence and user defined threshold using the concept of Venn diagrams. The proposed approach scans the temporal database only once to find the temporal association patterns and hence reduces the huge overhead incurred when the database is scanned multiple times.

[1]  Aoying Zhou,et al.  Mining Frequent Items in Spatio-temporal Databases , 2004, WAIM.

[2]  Sushil Jajodia,et al.  Temporal Databases: Theory, Design, and Implementation , 1993 .

[3]  Vangipuram Radhakrishna,et al.  A Survey on Temporal Databases and Data mining , 2015 .

[4]  Ming-Syan Chen,et al.  On mining general temporal association rules in a publication database , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  J. S. Yoo.  Temporal Data Mining: Similarity-Profiled Association Pattern , 2012 .

[6]  Long Jin,et al.  Discovery of Temporal Frequent Patterns Using TFP-Tree , 2006, WAIM.

[7]  Toon Calders,et al.  Axiomatization of frequent itemsets , 2003, Theor. Comput. Sci..

[8]  Gultekin Özsoyoglu,et al.  Temporal and Real-Time Databases: A Survey , 1995, IEEE Trans. Knowl. Data Eng..

[9]  Jouni K. Seppänen,et al.  Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data , 2004, Database Support for Data Mining Applications.

[10]  Wolfgang Lehner,et al.  COMBI-Operator: Database Support for Data Mining Applications , 2003, VLDB.

[11]  Shashi Shekhar,et al.  Similarity-Profiled Temporal Association Mining , 2009, IEEE Transactions on Knowledge and Data Engineering.

[12]  Shashi Shekhar,et al.  Mining Temporal Association Patterns under a Similarity Constraint , 2008, SSDBM.

[13]  Dimitrios Gunopulos,et al.  Mining frequent arrangements of temporal intervals , 2009, Knowledge and Information Systems.

[14]  Susan P. Imberman,et al.  Discovery of Association Rules in Temporal Databases , 2007, Fourth International Conference on Information Technology (ITNG'07).

[15]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[16]  Matteo Golfarelli,et al.  A Survey on Temporal Data Warehousing , 2009, Int. J. Data Warehous. Min..