solved using hybrid of supervised and unsupervised learning known as semisupervised learning we will explore this area in greater detail later in the book successful applications of unsupervised learning in the last ten yearsmost successful commercial applications of machine learning have come from the supervised learning spacebut this is changing unsupervised learning applications have become more commonplace sometimesunsupervised learning is just means to make supervised applications better other timesunsupervised learning achieves the commercial application itself here is closer look at two of the biggest applications of unsupervised learning to dateanomaly detection and group segmentation anomaly detection performing dimensionality reduction can reduce the original high-dimensional feature space into transformed lower-dimensional space in this lower-dimensional spacewe find where the majority of points densely lie this portion is the normal space points that lie much farther away are called outliers--or anomalies--and are worth investigating in greater detail anomaly detection systems are commonly used for fraud detection such as credit card fraudwire fraudcyber fraudand insurance fraud anomaly detection is also used to identify raremalicious events such as hacking of internet-connected devicesmaintenance failures in mission-critical equipment such as airplanes and trainsand cybersecurity breaches due to malware and other pernicious agents we can use these systems for spam detectionsuch as the email spam filter example we used earlier in the other applications include finding bad actors to stop activity such as terrorist financingmoney launderinghuman and narcotics traffickingand arms dealingidentifying high risk events in financial tradingand discovering diseases such as cancer to make the analysis of anomalies more manageablewe can use clustering algorithm to group similar anomalies together and then hand-label these clusters based on the types of behavior they represent with such systemwe can have an unsupervised learning ai that is able to identify anomaliescluster them into appropriate groupsandusing the cluster labels provided by humansrecommend to business analysts the appropriate course of action with anomaly detection systemswe can take an unsupervised problem and eventually create semisupervised one with this cluster-and-label approach over timewe can run supervised algorithms on the labeled data alongside the unsupervised successful applications of unsupervised learning