Anomaly refers to any inconsistent or redundant data point that appears distinct from the baseline pattern. It usually happens when data deviates from the established dataset for various reasons, e.g., incomplete data uploading or unexpected data deletions in the database.
Anomaly detection refers to monitoring the dataset using automated tools that record multiple observations to show the behavior of data points under set circumstances. It is the process of identifying the outliers and fixing them to avoid flaws in the dataset.
Companies train anomaly detection models by providing them with the sample dataset as training data. This data is then processed, and the tool generates the algorithm to discern between what is normal and what is an anomaly. In case of insufficient data, machine learning allows the system to determine the baseline by feeding the observations and developing a detection model. Once the range for data variation is defined, it identifies the data points that go beyond it.
It is difficult to manually monitor large datasets. Therefore, companies use anomaly detection in data mining to fetch data trends and observe the results to identify the data points that are deviating from the optimal pattern. Identifying anomalous data points is an efficient way to recognize the problems in the dataset and get them resolved as soon as possible.
There are various supervised and unsupervised techniques that are used to detect anomalies in datasets. Data can be visualized using libraries like Bokeh and Plotly to represent datasets and show anomalous data points.
Let's take a look at some of the unsupervised techniques for anomaly detection.
Isolation forest uses the random forest algorithm to detect the outliers in a dataset. In this technique, the data points are split in order to record isolated observations for all the data points.
Note: The outlying data points are easier to isolate compared to the inlying data points that are difficult to isolate.
Let's visualize the isolations for the outlying and inlying data points and observe the difference.
It is observable in the visual above that the outliers are easy to isolate and require fewer partitions. However, the inliers require a lot of partitions and sub-partitions, and still, it is difficult to isolate them.
Learn further about the outlier detection with isolation forest.
The local outlier factor is a density-based technique that identifies the outliers based on the neighbors. The data points that are lying in areas with lower density as compared to the neighbors are considered anomalous.
Let's visualize the data for the inliers and outliers and observe the difference.
It can be seen in the visual above that the normal data points are in forming clusters, and the less dense neighboring area around them have outliers. This technique uses a proper formula to identify the outliers by using the density as a variable.
Learn further about the outlier detection with the local outlier factor.
Robust covariance is used to detect anomalies in the datasets with Gaussian distribution.
Note: In Gaussian distribution, data points away from the 3rd deviation are likely to be considered as anomalies.
Let's visualize the covariance for the anomalous and inlying data points and observe the difference.
It can be seen in the visual above that the normal data points are in concentration at one place, and the anomalous data points are away from them and can easily be identified.
Learn further about outlier detection with robust covariance.
Unavailability of 100 percent data set: Despite feeding data to the model that covers the majority of the use cases, it is impossible to know what every kind of anomaly looks like. Therefore, it is impossible to prepare a labeled dataset that can anticipate every single type of anomaly, even if there are sufficient resources.
Noise handling: To overcome the challenge stated above, if we use unsupervised techniques, they can be sensitive to noise. This can result in faulty anomaly detection, and noise cancellation can be resource intensive.
Setting a proper threshold: Even if the noise is handled, it is challenging to set an accurate threshold to define normal and anomalous data points in unsupervised techniques. It often comes with trade-offs and is dependent on the application that is being used.
Anomaly detection is a crucial process as it highly influences the accuracy of our system in machine learning. To detect anomalies, there are various supervised and unsupervised techniques. A few of the unsupervised techniques include isolation forest, local outlier factor, and robust covariance.
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