Optimal usage of pessimistic association rules in cost effective decision making

Strength of an association rule is expressed in terms of interest between antecedent and consequent which is measured by using objective interestingness measures. The rules that contain at least user defined minimum interestingness are treated as Strong Association Rules (SAR). All of the SARs, extracted from the same database have no equal importance in knowledge discovery. As the user has limited resource to fix up the minimum interestingness, so there is a chance of generation of some SARs with high dissociation. Conceptually dissociation is opposite of association. As a matter of fact this type of SARs has pessimistic significance in knowledge discovery. It seems that some of the SARs are not really strong rules. This paper tries to find out such type of pseudo SARs termed as Pessimistic Association Rules (PAR). Dealing with PARs may not satisfy the user's expectation in decision making. Thus rejection of PARs may lead to a cost effective way to get a concrete decision. Experimental analysis proves the effectiveness of the proposed concept.

[1]  Nilanjan Dey,et al.  Indian Sign Language Recognition Using Optimized Neural Networks , 2015, ITITS.

[2]  Shouvik Chakraborty,et al.  An Elitist Model for Obtaining Alignment of Multiple Sequences using Genetic Algorithm , 2015 .

[3]  Daniel Sánchez,et al.  A Logic Approach for Exceptions and Anomalies in Association Rules , 2008, SOCO 2008.

[4]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[5]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[6]  Soumya Sen,et al.  Towards Golden Rule of Capital Accumulation: A Genetic Algorithm Approach , 2017, CISIM.

[7]  Nilanjan Dey,et al.  Counting all possible simple paths using Artificial Cell division mechanism for directed acyclic graphs , 2015, 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

[8]  Nilanjan Dey,et al.  Application of cuckoo search in water quality prediction using artificial neural network , 2017, Int. J. Comput. Intell. Stud..

[9]  Kalyani Mali,et al.  Trust: A new objective measure for symmetric association rule mining in account of dissociation and null transaction , 2017, 2016 Eighth International Conference on Advanced Computing (ICoAC).

[10]  Geert Wets,et al.  Using association rules for product assortment decisions: a case study , 1999, KDD '99.

[11]  Nilanjan Dey,et al.  Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[12]  Kalyani Mali,et al.  Ranking of association rules toward smart decision for smart city , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[13]  Nilanjan Dey,et al.  Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach , 2016, FICTA.

[14]  Sukomal Pal,et al.  Association against dissociation: some pragmatic considerations for frequent itemset generation under fixed and variable thresholds , 2005, SKDD.

[15]  C. Becquet,et al.  Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data , 2002, Genome Biology.

[16]  Subrata Bose,et al.  Mining and Ranking Association Rules in Support, Confidence, Correlation, and Dissociation Framework , 2015, FICTA.

[17]  Nilanjan Dey,et al.  Electrical Energy Output Prediction Using Cuckoo Search Based Artificial Neural Network , 2018 .

[18]  Nilanjan Dey,et al.  Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings , 2016, Neural Computing and Applications.

[19]  Shouvik Chakraborty,et al.  An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing , 2015 .

[20]  Shouvik Chakraborty,et al.  Blending roulette wheel selection with simulated annealing for job shop scheduling problem , 2015 .

[21]  Soumya Sen,et al.  Cuckoo search coupled artificial neural network in detection of chronic kidney disease , 2017, 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech).

[22]  K Krishna,et al.  Job Shop Scheduling using Distributed Simulated Annealing Algorithms , 1992 .

[23]  Amira S. Ashour,et al.  Neural-based prediction of structural failure of multistoried RC buildings , 2016 .

[24]  Jaideep Srivastava,et al.  Selecting the right objective measure for association analysis , 2004, Inf. Syst..

[25]  Sankhadeep Chatterjee,et al.  Analysis of Different Feature Description Algorithm in object Recognition , 2017 .

[26]  Nilanjan Dey,et al.  A Vulnerability Analysis Mechanism Utilizing Avalanche Attack Model for Dependency-Based Systems , 2016 .

[27]  Soumya Sen,et al.  Non-dominated sorting genetic algorithm — II supported neural network in classifying forest types , 2017, 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech).

[28]  Jun Tan.  Different Types of Association Rules Mining Review , 2012 .

[29]  Sankhadeep Chatterjee,et al.  A quality prediction method for weight lifting activity , 2015 .

[30]  Nilanjan Dey,et al.  An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding , 2016 .

[31]  Nilanjan Dey,et al.  Efficient Economic Profit Maximization: Genetic Algorithm Based Approach , 2018 .

[32]  Nilanjan Dey,et al.  Forest Type Classification: A Hybrid NN-GA Model Based Approach , 2016 .

[33]  Sankhadeep Chatterjee,et al.  A Novel Boolean Expression based Algorithm to find all possible Simple Paths between two nodes of a Graph , 2014 .

[34]  Nilanjan Dey,et al.  Hybrid Non-Dominated Sorting Genetic Algorithm: II-Neural Network Approach , 2018 .