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ITS 632 Auburn University Main Campus Chapter 5 Association Rule Discussion


When thinking about the association rule, answer the following questions this week. 

  1. What is the association rule in data mining?
  2. Why is the association rule especially important in big data analysis?
  3. How does the association rule allow for more advanced data interpretation?


The association rule is a rule that lays out a specific type of data mining process and is used to determine associates between items (Abdel-Basset, et al., 2018). These associates are determined by a specific set of rules around a specified threshold (Abdel-Basset, et al., 2018). This provides researchers with a valuable tool for data mining and analysis as it allows them to effectively determine desired associations.

2. The association rule is especially important in big data analysis because of its ability to effectively construct real and efficient decision-making system (Abdel-Basset, et al., 2018). The association rule has proven to be more effective than the previous use of fuzzy association rules and because of this provides a use in many applications. This could prove effective in increasing the quality and discovery for researchers in future research.

3. There are several ways in which the association rule allows for more advanced data interpretation. These include making use of indexing and query processing capabilities, and exploiting the DBMS support for scalability, check-pointing, and parallelization (Tan, et al., 2018). These processing capabilities allow for more advanced data interpretation by providing researchers and analysts with a way to find important associations quickly and precisely.



Association Rule in the data mining is a method consisting of rule-based mechanism with “if-then”/”if-then-else” statements. Association rule is mainly used to discover the probability of interesting relationships among the variables in large data sets or big data. Rules are a set of frequent teems which represent the uncovered relationships within the entire data set. It has been used for data mining in recent years and its applications had created many unknown data driven insights in several fields which had discovered new knowledge in various fields. By determining the relationships among different variables, association rule has become a chosed methodology in data mining analysis.


Association Rule has high importance in the data analysis as it finds the correlations and co-occurances among dependent and independent variables. It helps in creating patterns, trends in relational, transational databases and real time big data. Association rule had created a new dimension in data analysis in finding interesting and fascinating relationships among different variables within the datasets. it has tremendous applications in a number of sectors like healthcare, research, business, transportation, infrastructures and so on where big data is collected in a daily basis. It can answer many practical questions and helps in high level decision making process. For example, the correlation patterns can help to determine the strong positive or negative relationships among various determinants which can help the organizations to cater their incentives or productivity.


Association rule allows for more advanced data interpretations. For example in medical research, physicians can determine several relationships to find the cause of a disease and can create new patterns to predict upcoming diseases. Similarly in business, retail sales data can be analyzed with the association rule to figure out customer purchase habits and improve the business strategies accordingly. Advanced association rule can be applied to find sequential patterns with more than one dependent variables and association rules. This would help in finding complex patters or trends which are not easily determined. Higher algorithims may be used to create sequential patterns picking two or more independent variables and finding the outcomes.


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