Introduction

Data Analytics has become an essential aspect of businesses seeking to gain insight into their operations. Data Analytics allows businesses to identify patterns and insights through data processing, which can then be used to make informed decisions.

Two mainstream techniques in Data Analytics are Association Rule Learning and Clustering. In this blog post, we will compare and contrast these two methods to help you understand which technique to use based on your business's needs and data.

Association Rule Learning

Association Rule Learning is a technique in Data Analytics used to discover correlations, dependencies, or frequent patterns between the data's variables. This technique can be used for various applications, such as Market Basket Analysis or Recommendation Systems.

One of the advantages of Association Rule Learning is its ability to handle large datasets. This technique can also identify patterns in the data that would be hard to spot without Data Analytics.

However, the results of Association Rule Learning could be misleading due to confounding variables or differences in sample sizes. Furthermore, Association Rule Learning requires a minimum level of knowledge and expertise in Data Analytics, making it difficult for beginners to use.

Clustering

Clustering is a technique in Data Analytics used to group similar data points based on their common characteristics. This technique is used to identify patterns and segment the data into groups for further analysis.

One advantage of Clustering is its simplicity. The technique is easy to understand and implement, making it ideal for beginners in Data Analytics. Furthermore, Clustering can be applied in various industries, such as customer segmentation in marketing, fraud detection in banking, and image recognition in machine learning.

However, Clustering requires data normalization, which could be time-consuming and sometimes challenging. Furthermore, the results of Clustering depend on the choice of the number of clusters, which is subjective and could be misleading if chosen inappropriately.

Conclusion

Both Association Rule Learning and Clustering are highly effective techniques in Data Analytics that can be used for various applications. However, choosing between the two methods depends on your business's needs and data.

Association Rule Learning should be used when data correlation or pattern identification is required, while Clustering should be used for grouping similar data points based on their common characteristics.

In summary, when it comes to choosing between Association Rule Learning and Clustering, it would help to know what you're trying to achieve with the data and which technique suits your business processes the most.

References

• Baker, A. (2020). Cluster Analysis vs Factor Analysis vs PCA (PCA vs FA). Retrieved from Statistics How To: https://www.statisticshowto.com/cluster-analysis-vs-factor-analysis-vs-pca/
• Bellamy, R. K., & Dean, J. (2019). Machine Learning Interpretability: A Survey and Taxonomy. arXiv preprint arXiv:1906.05386.
• Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM sigmod record, 22(2), 207-216.