Data Mining Techniques

5. Outlier detection

Outlier detection determines any anomalies in datasets. Once organizations find aberrations in their data, it becomes easier to understand why these anomalies happen and prepare for any future occurrences to best achieve business objectives. For instance, if there’s a spike in the usage of transactional systems for credit cards at a certain time of day, organizations can capitalize on this information by figuring out why it’s happening to optimize their sales during the rest of the day.

6. Clustering

Clustering is an analytics technique that relies on visual approaches to understanding data. Clustering mechanisms use graphics to show where the distribution of data is in relation to different types of metrics. Clustering techniques also use different colors to show the distribution of data.

Graph approaches are ideal for using cluster analytics. With graphs and clustering in particular, users can visually see how data is distributed to identify trends that are relevant to their business objectives.

7. Regression

Regression techniques are useful for identifying the nature of the relationship between variables in a dataset. Those relationships could be causal in some instances, or just simply correlate in others. Regression is a straightforward white box technique that clearly reveals how variables are related. Regression techniques are used in aspects of forecasting and data modeling.

8. Prediction

Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into the future. Thus, it gives organizations insight into what trends will happen next in their data. There are several different approaches to using predictive analytics. Some of the more advanced involve aspects of machine learning and artificial intelligence. However, predictive analytics doesn’t necessarily depend on these techniques —it can also be facilitated with more straightforward algorithms.

9. Sequential patterns

This data mining technique focuses on uncovering a series of events that takes place in sequence. It’s particularly useful for data mining transactional data. For instance, this technique can reveal what items of clothing customers are more likely to buy after an initial purchase of say, a pair of shoes. Understanding sequential patterns can help organizations recommend additional items to customers to spur sales.

10. Decision trees

Decision trees are a specific type of predictive model that lets organizations effectively mine data. Technically, a decision tree is part of machine learning, but it is more popularly known as a white box machine learning technique because of its extremely straightforward nature.

A decision tree enables users to clearly understand how the data inputs affect the outputs. When various decision tree models are combined they create predictive analytics models known as a random forest. Complicated random forest models are considered black box machine learning techniques, because it’s not always easy to understand their outputs based on their inputs. In most cases, however, this basic form of ensemble modeling is more accurate than using decision trees on their own.

11. Statistical techniques

Statistical techniques are at the core of most analytics involved in the data mining process. The different analytics models are based on statistical concepts, which output numerical values that are applicable to specific business objectives. For instance, neural networks use complex statistics based on different weights and measures to determine if a picture is a dog or a cat in image recognition systems.

Statistical models represent one of two main branches of artificial intelligence. The models for some statistical techniques are static, while others involving machine learning get better with time.

12. Visualization

Data visualizations are another important element of data mining. They grant users insight into data based on sensory perceptions that people can see. Today’s data visualizations are dynamic, useful for streaming data in real-time, and characterized by different colors that reveal different trends and patterns in data.

Dashboards are a powerful way to use data visualizations to uncover data mining insights. Organizations can base dashboards on different metrics and use visualizations to visually highlight patterns in data, instead of simply using numerical outputs of statistical models.

13. Neural networks

A neural network is a specific type of machine learning model that is often used with AI and deep learning. Named after the fact that they have different layers which resemble the way neurons work in the human brain, neural networks are one of the more accurate machine learning models used today.

Although a neural network can be a powerful tool in data mining, organizations should take caution when using it: some of these neural network models are incredibly complex, which makes it difficult to understand how a neural network determined an output.

14. Data warehousing

Data warehousing is an important part of the data mining process. Traditionally, data warehousing involved storing structured data in relational database management systems so it could be analyzed for business intelligence, reporting, and basic dashboarding capabilities. Today, there are cloud data warehouses and data warehouses in semi-structured and unstructured data stores like Hadoop. While data warehouses were traditionally used for historic data, many modern approaches can provide an in-depth, real-time analysis of data.