MIS 661 Topic 2 DQ 2

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Clustering is purely descriptive. Clustering groups the items according to how similar they are, and it does so in an unguided (unsupervised) manner. There are two primary benefits of clustering. Please discuss them with examples and your insight from researching the topic externally. Clustering is a powerful unsupervised machine learning technique used to group similar data points together based on their characteristics. It has numerous benefits across various fields such as data exploration and understanding, data preprocessing and feature engineering, machine learning model training and evaluation, customer segmentation and personalization, image and text analysis, anomaly detection and fraud detection. Benefits of Clustering: 1. Identifying unusual or suspicious patterns in data Clustering algorithms can help identify patterns in data that are unusual or suspicious, which can be useful in detecting fraudulent activities or outliers in financial transactions. Clustering can be used to identify patterns in financial transactions that may indicate fraudulent activity, such as a sudden increase in transaction volume or a series of transactions to unusual or high-risk locations. By analyzing historical transaction data, clustering can help identify unusual patterns that may indicate fraud, allowing financial institutions to take action to prevent further losses. For example, Tax Evasion Detection in the US. IRS uses machine learning to detect fraudulent tax returns. The machine learning algorithms analyze large amounts of data, including income, deductions, and past tax history, to flag suspicious returns. Tax administrators could use clustering to create groups of similar tax returns based on the many variables contained in the dataset, enabling better categorization than a person might have discovered on their own. Another type of unsupervised learning that could facilitate tax enforcement is anomaly detection, which aims to detect novel or unusual data. Anomaly-detection methods include one- class support vector machines and isolation forests. Those methods can be used in combination with clustering, wherein outliers, or returns that deviate greatly from the rest in the cluster, may indicate noncompliance. 2. Gaining insights into the relationships between data points Clustering can also help gain insights into the relationships between data points. By grouping similar data points together, we can identify clusters of related data points and investigate the relationships between them.

This can help us identify trends and patterns in the data, and gain a better understanding of how different data points are related to each other. For example, fill an online shopping cart with diapers, strollers and sippy cups and the site just may recommend that you add a bib and a baby monitor to your order. This is an example of association, where certain features of a data sample correlate with other features. By looking at a couple of key attributes of a data point, an unsupervised learning model can predict the other attributes with which they’re commonly associated. Clustering can be used in any situation where there is a large amount of data and patterns need to be identified. To successfully implement clustering, businesses must have a clear understanding of their goals, data sources, and the tools and techniques available. Eric, J. (n.d.). Data RunDown. Why Do We Use Clustering? 5 Benefits and Challenges In Cluster Analysis https://datarundown.com/why-clustering/ Whittaker, C. (2019). DataFloq. 7 Innovative Uses of Clustering Algorithms in the Real World https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/

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