UTARM: an efficient algorithm for mining of utility-oriented temporal association rules

Recently, association rule mining has become an area of interest for research in the field of knowledge discovery and several algorithms have been established. Lately, for business development, data mining researchers have enhanced the quality of association rule mining for the mining of association patterns by integrating the influential factors, for instance temporal, value utility and more. Here, we have proposed an efficient algorithm, called UTARM Utility-Based Temporal Association Rule Mining, which combines both temporal time periods and utility for mining of remarkable and helpful association rules. The proposed algorithm can be able to mine utility-oriented temporal association rules by adapting the support with relevant to the time periods and utility. Furthermore, the scan time required for finding the FTU itemsets is considerably reduced. The experimentation is carried out on large data sets and the experimental results ensure that the proposed algorithm effectively discovers the utility-oriented temporal association rules.

[1]  M. Lakshmi,et al.  A strategy for mining utility based temporal association rules , 2010, Trendz in Information Sciences & Computing(TISC2010).

[2]  Vincent S. Tseng,et al.  An efficient algorithm for mining high utility itemsets with negative item values in large databases , 2009, Appl. Math. Comput..

[3]  Chin-Chen Chang,et al.  Two-Phase Algorithms for a Novel Utility-Frequent Mining Model , 2007, PAKDD Workshops.

[4]  Howard J. Hamilton,et al.  A Unified Framework for Utility Based Measures for Mining Itemsets , 2006 .

[5]  Tzung-Pei Hong,et al.  Applying the maximum utility measure in high utility sequential pattern mining , 2014, Expert Syst. Appl..

[6]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[7]  Unil Yun,et al.  Efficient mining of weighted interesting patterns with a strong weight and/or support affinity , 2007, Inf. Sci..

[8]  Young-Koo Lee,et al.  Mining Weighted Frequent Patterns Using Adaptive Weights , 2008, IDEAL.

[9]  A. Choudhary,et al.  A fast high utility itemsets mining algorithm , 2005, UBDM '05.

[10]  Ming-Syan Chen,et al.  Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[11]  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.

[12]  Ming-Syan Chen,et al.  Mining general temporal association rules for items with different exhibition periods , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[13]  John F. Roddick,et al.  ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data , 2007, Data Knowl. Eng..

[14]  Beth Plale,et al.  Temporal representation for mining scientific data provenance , 2014, Future Gener. Comput. Syst..

[15]  Aziz Guergachi,et al.  Context Based Positive and Negative Spatio-Temporal Association Rule Mining , 2013, Knowl. Based Syst..

[16]  Ming-Syan Chen,et al.  Twain: Two-end association miner with precise frequent exhibition periods , 2007, TKDD.

[17]  T. Purusothaman,et al.  A Novel Utility Sentient Approach for Mining Interesting Association Rules , 2009 .

[18]  M. Sulaiman Khan,et al.  A Weighted Utility Framework for Mining Association Rules , 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.

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

[20]  Wei Liu,et al.  Frequent patterns mining in multiple biological sequences , 2013, Comput. Biol. Medicine.

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

[22]  James H. Faghmous,et al.  Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities , 2014 .

[23]  Hans-Peter Kriegel,et al.  Managing uncertainty in spatial and spatio-temporal data , 2014, 2014 IEEE 30th International Conference on Data Engineering.

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

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

[26]  Young-Koo Lee,et al.  Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases , 2009, IEEE Transactions on Knowledge and Data Engineering.

[27]  Kanak Saxena.  Notice of Violation of IEEE Publication PrinciplesEfficient Mining of Weighted Temporal Association Rules , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[28]  Fan Wu,et al.  An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports , 2013, J. Syst. Softw..

[29]  Ashok Kumar Das,et al.  An efficient approach for mining association rules from high utility itemsets , 2015, Expert Syst. Appl..

[30]  Tzung-Pei Hong,et al.  An efficient method for mining non-redundant sequential rules using attributed prefix-trees , 2014, Eng. Appl. Artif. Intell..

[31]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2001, Proceedings Eighth International Symposium on Temporal Representation and Reasoning. TIME 2001.

[32]  Xindong Wu,et al.  PMBC: Pattern mining from biological sequences with wildcard constraints , 2013, Comput. Biol. Medicine.

[33]  Cory J. Butz,et al.  A Foundational Approach to Mining Itemset Utilities from Databases , 2004, SDM.

[34]  Ajith Abraham,et al.  An efficient algorithm for incremental mining of temporal association rules , 2010, Data Knowl. Eng..

[35]  Parvinder S. Sandhu,et al.  An Improvement in Apriori Algorithm Using Profit and Quantity , 2010, 2010 Second International Conference on Computer and Network Technology.

[36]  Ömer M. Soysal,et al.  Association rule mining with mostly associated sequential patterns , 2015, Expert Syst. Appl..

[37]  Sourav S. Bhowmick,et al.  Association Rule Mining: A Survey , 2003 .

[38]  Bianca Zadrozny,et al.  UBDM 2006: Utility-Based Data Mining 2006 workshop report , 2006, SKDD.

[39]  Otto Huisman,et al.  Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach , 2014, Int. J. Geogr. Inf. Sci..

[40]  T Purusothaman,et al.  UTILITY SENTIENT FREQUENT ITEM SET MINING AND ASSOCIATION RULE MINING: A LITERATURE SURVEY AND COMPARATIVE STUDY , 2009 .