Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

Main Article Content

Dewi Wisnu Wardani


This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obtained rules from positive association rules and negative association rules strengthens to each other with a pretty good confidence score.


Download data is not yet available.

Article Details

How to Cite
Wardani DW. Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis. Baghdad Sci.J [Internet]. [cited 2021Mar.4];18(3):0554. Available from:


Kaur M, Kang S. Market Basket Analysis: Identify the changing trends of market data using association rule mining. Procedia Comput Sci. 2016;85:78–85.

Ohri M, Thakur K. An Enhanced Apriori and Improved Algorithm for Association Rules. 2016.

Saito H, Monden A, Yücel Z. Extended association rule mining with correlation functions. In: 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD). IEEE; 2018. p. 79–84.

Gogtay N, Thatte U. Principles of correlation analysis. J Assoc Physicians India. 2017;65(3):78–81.

Said AM, Dominic P, Zailani S. A new scheme for extracting association rules: market basket analysis case study. Int J Bus Innov Res. 2011;6(1):28–46.

Bagui S, Dhar PC. Mining positive and negative association rules in Hadoop’s MapReduce environment. In: Proceedings of the ACMSE 2018 Conference. 2018. p. 1–1.

Li C, Hao F, Zhao L, Song L, Dong X. Analysis of medical and healthcare data based on positive and negative association rules. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE; 2017. p. 1559–1564.

Han J, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier; 2011.

Gara GPP, Padao FRF. Mining Association Rules on Students Profiles and Personality Types. In: Proceedings of the International Multiconference of Engineers and Computer Scientists. 2015.

Sarno R, Dewandono RD, Ahmad T, Naufal MF, Sinaga F. Hybrid Association Rule Learning and Process Mining for Fraud Detection. IAENG Int J Comput Sci. 2015;42(2).

Zeng N, Xiao H. Inferring implications in semantic maps via the Apriori algorithm. Lingua. 2020 Feb 1:102808.

Sakai H, Nakata M, Watada J. NIS-Apriori-based rule generation with three-way decisions and its application system in SQL. Inf Sci. 2020;507:755–771.

John M, Shaiba H. Apriori-Based Algorithm for Dubai Road Accident Analysis. Procedia Comput Sci. 2019;163:218–227.

Redhu S, Hegde RM. Optimal relay node selection in time-varying IoT networks using apriori contact pattern information. Ad Hoc Netw. 2020;98:102065.

Kadir ASA, Bakar AA, Hamdan AR. Frequent absence and presence itemset for negative association rule mining. In: Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on. IEEE; 2011. p. 965–970.

Piao X, Wang Z, Liu G. Research on mining positive and negative association rules based on dual confidence. In: Internet Computing for Science and Engineering (ICICSE), 2010 Fifth International Conference on. IEEE; 2010. p. 102–105.

Martin D, Rosete A, Alcala-Fdez J, Herrera F. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. IEEE Trans Evol Comput. 2014;18(1):54–69.

Shaheen M, Shahbaz M, Guergachi A. Context based positive and negative spatio-temporal association rule mining. Knowl-Based Syst. 2013;37:261–273.

Çokpınar S, Gündem Tİ. Positive and negative association rule mining on XML data streams in database as a service concept. Expert Syst Appl. 2012;39(8):7503–7511.

Wu X, Zhang C, Zhang S. Efficient mining of both positive and negative association rules. ACM Trans Inf Syst TOIS. 2004;22(3):381–405.

Zhou Q, Wang T, Basu K. Negative association between BMI and depressive symptoms in middle aged and elderly Chinese: Results from a national household survey. Psychiatry Res. 2018;269:571–578.

Houari A, Ayadi W, Yahia SB. Mining negative correlation biclusters from gene expression data using generic association rules. Procedia Comput Sci. 2017;112:278–287.

Jabbour S, El Mazouri FE, Sais L. Mining Negatives Association Rules Using Constraints. Procedia Comput Sci. 2018;127:481–488.

Xu L, Zhu D, Chen X, Li L, Huang G, Yuan L. Combination of one-dimensional convolutional neural network and negative correlation learning on spectral calibration. Chemom Intell Lab Syst. 2020 Apr 15;199:103954.

Balusu SK, Pinjari AR, Mannering FL, Eluru N. Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction. Anal Methods Accid Res. 2018;20:46–67.

Sheng W, Shan P, Chen S, Liu Y, Alsaadi FE. A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble. Neurocomputing. 2017;247:173–182.

Ma T, Wang C, Wang J, Cheng J, Chen X. Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China. Inf Sci. 2019;505:157–182.