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?

 

Association Rule

Association rules are the if/then statements used to find associations among independent data in information repositories. It is a process that helps to find recurrent causal structures, correlations, patterns, or associations from data sets in different databases including transactional and relational databases (Witten, Frank, Hall & Pal, 2016). The association rule tries to identify rules that allow the prediction of an existence of a particular item based on the existence of others within the transaction.

The main concept in the association rule is the mining of recurrent patterns. This helps establish the connection between items in a particular domain, which can be used in decision--making. Mining patterns from Big Data with huge data volumes has various challenges because of the heterogeneity of data, many data dimensions, and memory requirements. These complexities can be curtailed using association rules in data mining. The rule helps forecast and identify transactional actions depending on data from training transactions (Witten, Frank, Hall & Pal, 2016). Using the technique, people can address issues like what individuals intend to buy and the frequency in which they do. It can also help correlate or associate items and products.

Association rules are similar to classification rules, however, they can be used to predict all attributes and not only classes. This makes them flexible since they can also predict a combination of attributes. Moreover, unlike classification rules, they are not designed to be used as a set. Hence, different rules are used for different regularities that make a dataset, and they predict dissimilar things. Various association rules can be used in minute datasets, but they are used in large instances due to the high accuracy to those instances they apply (Witten, Frank, Hall & Pal, 2016). Besides, the rule’s coverage or support is the instance for which it forecasts appropriately. Its confidence or accuracy is the instances conveyed as a proportion of those it applies. Hence, the rule enables more advanced data analysis.

The association rule is crucial in various predictive applications and business analytics that are integrated into systems that give analysis services and prediction techniques. It is mostly suitable for evaluating the associations between objects, as it takes into consideration the interaction between data sets, and provides the rules of the if/then form (Ait-Mlouk, Agouti & Gharnati, 2017). Moreover, the rule has been used effectively in several areas such as medical diagnosis, text mining, business planning, telecommunications, and medical research.