Within data mining, anomaly detection plays a major role. It’s a key step in the process of machine learning that can help identify data points and events that trigger alerts. Anomaly detection also does so in real-time. That way, it makes for easier intrusion detection and it’s simpler to locate and address anomalous data or unlabeled data.
When a data point deviates from a project or dataset’s typical behavioral baseline, the anomalous data can signify critical incidents. As anomaly detection spots these changes from normal behavior, it can be easier to track changes to your business and analyze them accordingly. To learn the function of anomaly detection and how it spots outliers, here’s what you need to know.
What is an anomaly?
So, what is anomaly detection? Better yet, what is an anomaly? Within a given dataset, there is a baseline that determines whether or not your business is running as expected. There are patterns across your dataset that can be parsed and interpreted by machine learning. However, where there is “normal data,” there’s also abnormal data and outliers. An outlier or anomaly is any spike, data partition, or data point that crosses a dataset threshold. Any event or piece of data that deviates from your standard algorithm or projected graph is an anomaly or outlier. These unexpected items and anomalous data points simply mean that operations aren’t proceeding under the umbrella of business as usual.
This could entail intrusion detection, outlier analysis, fraud detection, or misuse detection in certain cases. However, anomaly detection capabilities can also display positive trends such as the success of a new marketing campaign or a solid sales promotion. It all depends on the algorithm that factors into your chosen anomaly detection system. However, in a given dataset, there need to be specific metrics that ensure smarter outlier detection. For instance, if your anomaly detection algorithm is picking up a spike in sales on a major shopping holiday, the algorithm isn’t working quite as intended. In your dataset, a sales jump on Black Friday or Cyber Monday is to be expected. This is why it’s important to understand that, categorically, anomalies are neither positive nor negative indicators. While they can point to fraud detection and dataset attacks, they can also point to growth and upward dataset momentum.
Why do businesses need anomaly detection?
Time series data, anomaly detection, and anomaly analysis are critical for many business operations. Between the various types of anomalies, including local anomalies, global anomalies, and collective anomalies, an anomaly detection system is helpful for many use cases. An anomaly detection system can be used for overall application and streaming performance and data patterns. This is a practical application of an anomaly detection system that many businesses use. Another basic idea is the inclusion of a classifier or anomaly detection technique for product quality. You’ll certainly use different datasets for this than you would the visualization of application growth.
An anomaly detection technique can also spot the root cause of a high outlier score beyond your standard deviation when it comes to the user experience. Dips and spikes in UX need to be addressed by both time series data and multivariate data. When it comes to the deployment of an anomaly detection algorithm for your data mining procedures, it’s important to have the right anomaly detection algorithm.
Chances are, you have a wide variety of complex systems that are interconnected and your detection systems need to be able to access key datasets, spot significant anomalies, unexpected changes, and unexpected behavior. If you need the ability to gain insights into abnormal behavior in your application domains and UX products, it’s time to incorporate the right anomaly detection algorithm into your brand.