Why Outlier Detection Is Crucial In Big Data

Why Outlier Detection Is Crucial In Big Data

Outlier detection is nothing new. It has been used for ages and is deployed to detect points considered ‘the odd man out.’ But why is outlier detection so important? It is a valuable tool in many practical use cases. Fraud detection and other similar aspects that diverge from the established pattern can be quickly and firmly established with outlier detection.

Outlier detection plays a central role in various data mining tasks. The purpose is to identify those specific parameters affected by outlier tools from thousands of other parameters. There are several methods for outlier detection.

There have been several name changes for comparable functions throughout the years. Apart from anomaly detection, these capabilities are also identified as

  • Deviation detection
  • Noise detection
  • Exception detection

The action behind these terms may be different. However, the technique involved in them is the same, more or less.

The Grubbs definition of outlier detection is as follows:

“An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs.”

As per the definition, an outlier appears to stand apart from other members in the data set. The outlier detection techniques can uncover probable outliers. However, human intervention is necessary to decode and accurately understand the results.

Outlier Detection in Big Data Settings

What Grubbs stated a few decades today is vastly different from the ground situation today. 

Today’s rapid increases in data production and data quantity need equally rapid and accurate data processing.

In the modern world, at any given time, there is a constant generation of social media posts, videos, text messages, and transactions. Consequently, outlier detection techniques must develop the proficiency to process data, if not in real time, at least closer to it. Potential outliers must be pointed out instantly because, in most cases, the information is time-specific, and it is imperative to take immediate corrective action. 

There is little doubt that Big Data has transformed the character of outlier detection. It has also resulted in more opportunities as we can get more value from outlier detection techniques. A larger data set can bring more value through the outlier detection process. 

Some interesting examples of outlier detection:

  • Helps detect unauthorized transactions using stolen or fake credit card
  • Points out false insurance claims or financial data
  • Alerts unauthorized access or attempts to access computer systems and networks.
  • Monitors activity related to dubious phone calls and harmful messages.
  • Helps identify production flaws or deviations from product characteristics that do not fit established standards.
  • Reveals changed imagery that can be used in medical scans to detect certain diseases.

Best Outlier Detection Techniques To Use On Big Data

The following are the main categories in which algorithms detect outliers.

Numerical Simulations

Firstly, the statistical or numerical outlier recognition system is one of the earliest and still the most commonly used techniques for outlier detection. In most cases, statistical probability provides the foundation for detecting outliers. It works on the basis that a specific event is so unlikely to happen that if it occurs, it must be an outlier. 

Proximity Based Techniques

Multivariate or bivariate data sets are ideal for these methods. They are easy to use and provide results by recognizing the space between data points. A major drawback of this technique is its runtime. It is highly complex and is directly related to the data size. As a result, processing large quantities of Big Data can take an unusually long time.

Parametric Models

Parametric models offer the best solution for overcoming the runtime complexity issue. Generally, these models are best suited for those handling Big Data. In a parametric model, the intricacy related to the problem is proportional to the model size and not the data size. Additionally, users must know in advance if the data will fit within the model. Parametric outlier detection models are suitable for specific problems, but they cannot serve the purpose of resolving generic problems as the properties of the input data set are unknown.

Neural Networks

Generally, this method of outlier detection brings cutting-edge technology to the process of selection and the identification of outliers.  The model-based nature of these networks makes them well suited for use with data patterns that are uncommon. As a result, they are best suited to address more general situations and instances where the input data structure is unspecified. Internally, the neural network employs outlier detection, classification, and grouping strategies.

This article provides a comprehensive look at the most common outlier detection methods used in the realm of Big Data. We can clearly see that most of the current outlier detection techniques used today are based on statistical, distance-based, and parametric approaches. As data volume and speed continue to grow, Neural Network approaches will become more popular for spotting anomalies.