Data Mining 2-question

Bhargav Reddy Mandadi 

Week 5 Discussion

COLLAPSE

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Cluster analysis or simply referred to as clustering groups items so that similar objects are placed in the same cluster and different objects in different groups (Deshmukh & Gulhane, 2016). Cluster analysis is a data mining tool used by organizations and businesses to identify groups of customers that exist, identify types of transactions that help identify fraud, and identify groups of behaviors that exist. Insurance companies deploy cluster analysis in identifying fraud. Cluster analysis works by associating objects by their closeness. Clustering is an unsupervised learning algorithm since the number of groups is not known before conducting the analysis. The similarity between objects is used in classifying and placing objects into groups.

Cluster analysis has been applied extensively in marketing. Clustering is deployed in marketing to better personalize and target customers based on their shopping preferences and trends (Punj, 1983). Marketing using cluster analysis is performed by identifying characteristics of customers that are similar to others and creating campaigns that are similar and have been successful. Mathematical models are used in identifying similar customers according to characteristics of purchasing patterns, behaviors, and many more. Cluster analysis segments customers to achieve better targeting of customers based on their behavior and preferences.

Clustering enables segmenting customers on many dimensions and starting campaigns based on the customers’ characteristics. The clusters change when clustering runs, ensuring the accuracy of data since the actual status is reflected. Also, customers who are very different from each other are identified. Clustering plays an essential role in grouping items into groups based on their similarities and is useful in grouping customers based on their behavior hence targeting them during marketing campaigns. Clustering is also used in detecting fraud in a group of transactions by picking anomalies.

References

Deshmukh, M. A., & Gulhane, R. (2016). Importance of Clustering in Data Mining. International Journal of Scientific & Engineering Research.

Punj, G. (1983). Cluster Analysis in Marketing Research: Review and Suggestions for Application. Journal of Marketing Research, https://doi.org/10.2307/3151680.

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