{"id":250,"date":"2022-03-29T10:45:00","date_gmt":"2022-03-29T10:45:00","guid":{"rendered":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/harini-jayaraman\/?p=250"},"modified":"2022-04-17T19:59:47","modified_gmt":"2022-04-17T19:59:47","slug":"anomaly-detection","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/harini-jayaraman\/anomaly-detection\/","title":{"rendered":"Anomaly detection"},"content":{"rendered":"\n

As we had an overview of one of the unsupervised learning method, K-means Clustering<\/em><\/strong>, in my previous blog<\/a> post, this post will introduce you to an another unsupervised learning method called Anomaly Detection. We should also note that Anomaly detection can be done using Supervised and Semi-supervised techniques as well. <\/p>\n\n\n\n

What is an Anomaly?<\/strong><\/h2>\n\n\n\n

Anomalies (Outliers) are patterns in data that do not conform to a well defined notion of normal behaviour. An unexpected change within these data patterns, or an event that does not conform to the expected data pattern, is considered an anomaly. In other words, an anomaly is a deviation from the usual happenings.<\/p>\n\n\n\n

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Figure 1., Wide Variety of Anomalies<\/em><\/figcaption><\/figure>\n\n\n\n
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Figure 2., Illustration of simple anomalies in <\/em>2D<\/figcaption><\/figure>\n
Source : Figure 1 ,<\/a> Figure 2<\/a><\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n

Red Occurences in Figure 1, describes the various anomalies that can occur. In Figure 2, We could observe most of the data occurences are in the region N_1 <\/span> and N_2 <\/span>. The points far off from these normal region, such as O_1, O_2 <\/span> and O_3 <\/span> are anomalies.<\/p>\n\n\n\n

Anomalies can occur for various reasons. Customer acquisition costs, Malicious activity, Web page views, Revenue per click, Credit card frauds and more. These are valuable metrics for any data analyst and is considered advantage in decision making process. Therefore, Anomaly detection is an important process, which if efficient and timely can enable intervention and action to minimize or avoid effects of underlying cause. Even from a statistical context, anomaly detection helps the methods to be more robust and efficient. <\/p>\n\n\n\n

Types of Anomalies<\/strong><\/h2>\n\n\n\n

Anomalies are majorly classified into these three main categories which I will discuss below. However, there are wide variety of anomalies. If you are interested in learning various anomaly types and how it is distinguished, this paper<\/a> could be helpful. <\/p>\n\n\n\n