Discovering calendar-based temporal association rules

A temporal association rule is an association rule that holds during specific time intervals. An example is that eggs and coffee are frequently sold together in morning hours. The paper studies temporal association rules during the time intervals specified by user-given calendar schemas. Generally, the use of calendar schemas makes the discovered temporal association rules easier to understand. An example of calendar schema is (year, month, day), which yields a set of calendar-based patterns of the form (d/sub 3/, d/sub 2/, d/sub 1/), where each d/sub i/ is either an integer or the symbol *. For example, (2000, *, 16) is such a pattern, which corresponds to the time intervals, each consisting of the 16th day of a month in year 2000. This paper defines two types of temporal association rules: precise-match association rules require that the association rule holds during every interval, and fuzzy-match ones require that the association rule holds during most of these intervals. The paper extends the well-known a priori algorithm, and also develops two optimization techniques to take advantage of the special properties of the calendar-based patterns. The experiments show that the algorithms and optimization techniques are effective.

[1]  Laks V. S. Lakshmanan,et al.  Exploratory mining via constrained frequent set queries , 1999, SIGMOD '99.

[2]  Masaru Kitsuregawa,et al.  Parallel mining algorithms for generalized association rules with classification hierarchy , 1997, SIGMOD '98.

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

[4]  Heikki Mannila,et al.  Discovering Frequent Episodes in Sequences , 1995, KDD.

[5]  Xiaodong Chen,et al.  Mining Temporal Features in Association Rules , 1999, PKDD.

[6]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[7]  Jiong Yang,et al.  TAR: temporal association rules on evolving numerical attributes , 2001, Proceedings 17th International Conference on Data Engineering.

[8]  Gustavo Rossi,et al.  An approach to discovering temporal association rules , 2000, SAC '00.

[9]  John F. Roddick,et al.  Adding Temporal Semantics to Association Rules , 1999, PKDD.

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

[11]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[12]  Xiaodong Chen,et al.  A Framework for Temporal Data Mining , 1998, DEXA.

[13]  Sridhar Ramaswamy,et al.  On the Discovery of Interesting Patterns in Association Rules , 1998, VLDB.

[14]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[15]  X.S. Wang,et al.  Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences , 1998, IEEE Trans. Knowl. Data Eng..

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

[17]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2003 .

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

[19]  Vipin Kumar,et al.  Scalable parallel data mining for association rules , 1997, SIGMOD '97.

[20]  Carla E. Brodley,et al.  KDD-Cup 2000 organizers' report: peeling the onion , 2000, SKDD.

[21]  John F. Roddick,et al.  A bibliography of temporal, spatial and spatio-temporal data mining research , 1999, SKDD.

[22]  John F. Roddick,et al.  YABTSSTDMR, Yet Another Bibliography of Temporal, Spatial, and Spatio-Temporal Data Mining Resear , 2001 .

[23]  Sushil Jajodia,et al.  Time Granularities in Databases, Data Mining, and Temporal Reasoning , 2000, Springer Berlin Heidelberg.

[24]  Renée J. Miller,et al.  Association rules over interval data , 1997, SIGMOD '97.

[25]  David Forster,et al.  A Representation for Collections of Temporal Intervals , 1986, AAAI.

[26]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..

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

[28]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

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

[30]  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).

[31]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.