Mining temporal association rules with frequent itemsets tree

Abstract A novel framework for mining temporal association rules by discovering itemsets with frequent itemsets tree is introduced. In order to solve the problem of handling time series by including temporal relation between the multi items into association rules, a frequent itemsets tree is constructed in parallel with mining frequent itemsets to improve the efficiency and interpretability of rule mining without generating candidate itemsets. Experimental results show that our algorithm can provide better efficiency and interpretability in mining temporal association rules in comparison with other algorithms and has good application prospects.

[1]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[2]  Suh-Yin Lee,et al.  CEMiner -- An Efficient Algorithm for Mining Closed Patterns from Time Interval-Based Data , 2011, 2011 IEEE 11th International Conference on Data Mining.

[3]  Chen Lu,et al.  A Fuzzy Calendar-Based Algorithm for Mining Temporal Association Rules and its Application , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[4]  Don-Lin Yang,et al.  Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals , 2014 .

[5]  Hui Ning,et al.  Research on Association Rules Mining with Temporal Restraint , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[6]  Peng Xu,et al.  Finding nuggets in IP portfolios: core patent mining through textual temporal analysis , 2012, CIKM '12.

[7]  Chloé Clavel,et al.  Using Temporal Association Rules for the Synthesis of Embodied Conversational Agents with a Specific Stance , 2016, IVA.

[8]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[9]  Mario A. Góngora,et al.  Web usage mining with evolutionary extraction of temporal fuzzy association rules , 2013, Knowl. Based Syst..

[10]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[11]  Jian Wang,et al.  Sequential pattern mining in databases with temporal uncertainty , 2017, Knowledge and Information Systems.

[12]  Guangchen Ruan,et al.  Parallel and quantitative sequential pattern mining for large-scale interval-based temporal data , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[13]  Aida Mustapha,et al.  Effect of Temporal Relationships in Associative Rule Mining for Web Log Data , 2014, TheScientificWorldJournal.

[14]  Imran N. Junejo,et al.  Dynamic scene understanding using temporal association rules , 2014, Image Vis. Comput..

[15]  Srinivasan Parthasarathy,et al.  Parallel Algorithms for Discovery of Association Rules , 1997, Data Mining and Knowledge Discovery.

[16]  Stefan Conrad,et al.  Mining Several Kinds of Temporal Association Rules Enhanced by Tree Structures , 2010, 2010 Second International Conference on Information, Process, and Knowledge Management.

[17]  Tzung-Pei Hong,et al.  An efficient projection-based indexing approach for mining high utility itemsets , 2012, Knowledge and Information Systems.

[18]  Asadullah Al Galib,et al.  Stock Trading Rule Discovery based on temporal data mining , 2010, International Conference on Electrical & Computer Engineering (ICECE 2010).

[19]  Akash Rajak,et al.  Modeling Clinical Database using Time Series based Temporal Mining , 2010 .

[20]  Guojun Mao,et al.  Mining Temporal Association Rules in Network Traffic Data , 2014 .

[21]  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).

[22]  Khushali Shah,et al.  EVALUATION ON DIFFERENT APPROACHES OF CYCLIC ASSOCIATION RULES , 2013 .

[23]  Christophe Rigotti,et al.  Efficient Spatio-temporal Mining of Satellite Image Time Series for Agricultural Monitoring , 2012, Trans. Mach. Learn. Data Min..

[24]  Iqbal Gondal,et al.  Mining Associated Patterns from Wireless Sensor Networks , 2015, IEEE Transactions on Computers.

[25]  Eddie Schwalb,et al.  Temporal Constraints: A Survey , 1998, Constraints.

[26]  Pan Yan.  A FP-tree Based Partition Mining Approach to Discovering Temporal Association Rules , 2006 .

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

[28]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[29]  Tzung-Pei Hong,et al.  Mining fuzzy temporal association rules by item lifespans , 2016, Appl. Soft Comput..