Temporal Association Rules Mining in T-databases Using Pipeline Technique

Temporal data mining is rapidly evolving area of research that is at the intersection of several disciplines, including statistic, temporal pattern recognition, temporal database, optimization visualization, high performance computing & parallel computing. The presence of a temporal association rule may suggest a number of interpretations, such as • Past event(PE) → Future event(FE) • The event(E) → PE and FE • events → coincidental (c) Classical association rules have no notion of order, while time implies an ordering. If we could find the associability of time with event, nothing will be hidden to us as the events are associated to each other in the form PE→PtE(present event)→FE. In this study, we examine the association rules mining in temporal database. After partitioning the database, a time interval TI=[s,e] is allocated to each partition and sequentially put the partitions in an array, in reverse order.

[1]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[2]  John F. Roddick,et al.  Temporal semantics in information systems - a survey , 1992, Inf. Syst..

[3]  Kanhaiya Lal,et al.  Mining association rules for clustered domains by separating disjoint sub-domains inLarge Databases , 2010 .

[4]  Xiaodong Chen,et al.  Discovering Temporal Association Rules in Temporal Databases , 1998, IADT.

[5]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[6]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[7]  Jiawei Han,et al.  An attribute-oriented approach for learning classification rules from relational databases , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[8]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[9]  Sushil Jajodia,et al.  Logical design for temporal databases with multiple granularities , 1997, TODS.

[10]  Ian Witten,et al.  Data Mining , 2000 .

[11]  Heikki Mannila,et al.  Methods and Problems in Data Mining , 1997, ICDT.

[12]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[13]  Willi Klösgen.  Deviation and Association Patterns for Subgroup Mining in Temporal, Spatial, and Textual Data Bases , 1998, Rough Sets and Current Trends in Computing.

[14]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[15]  Tomasz Imielinski,et al.  An Interval Classifier for Database Mining Applications , 1992, VLDB.

[16]  Sushil Jajodia,et al.  Discovering Temporal Patterns in Multiple Granularities , 2000, TSDM.

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

[18]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[19]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..

[20]  AgrawalRakesh,et al.  Mining association rules between sets of items in large databases , 1993 .

[21]  Sanjay Ranka,et al.  An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases , 1997, KDD.

[22]  John F. Roddick,et al.  A Survey of Temporal Knowledge Discovery Paradigms and Methods , 2002, IEEE Trans. Knowl. Data Eng..

[23]  Vincent S. Tseng,et al.  An efficient algorithm for mining temporal high utility itemsets from data streams , 2008, J. Syst. Softw..

[24]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[25]  Philip K. Chan,et al.  Systems for Knowledge Discovery in Databases , 1993, IEEE Trans. Knowl. Data Eng..

[26]  John F. Roddick,et al.  Database Issues in Knowledge Discovery and Data Mining , 1999, Australas. J. Inf. Syst..