Big data has resulted in an explosion in the use of extensive data mining techniques. Primarily due to an increase in the size of information that tends to be more varied and extensive in nature. Essentially, data mining is a process that involves tracking large banks of this information in order to generate newer information. Intuitively, one might be driven to assume that data mining is the extraction of new data. However, this isn’t the case - rather, it’s about extrapolating patterns and new knowledge from previously collected information. Relying on the technologies and techniques from the intersection of machine learning, statistics and database management, data mining specialists can process vast amounts of information and from them, draw impactful conclusions. But what are the techniques that drive data mining?

“Data will talk if you’re willing to listen”

Association Rule Learning

Association, rather referred to as relation, is probably the most familiar, better known straightforward data mining technique. Here, a simple correlation between two or more items, often of the same type to their identity patterns is required to discover interdependencies. It helps in identifying relationships between different variables in large databases. Association rules are vital in examining and forecasting customer relations and as such, it’s a technique that is highly recommended in the retail industry when trying to find patterns in point-of-sale data. It’s a great way to determine store layout, catalog design, product clustering and shopping basket analyses. Done correctly, it is a method guaranteed to increase conversion rate.

Anomaly/Outlier Detection

Anomaly detection is a process that involves searching for data items in a dataset that don’t match an expected behavior or projected pattern. An anomaly is an object that significantly deviates from the general average within a dataset. As such, it may also be referred to as an outlier, surprise, exception or contaminant. It is numerically distant from the rest of the data and used to indicate that something is out of the ordinary and needs additional analysis. Usually, anomaly detection is employed in uncovering risks or frauds within a critical system. With this regard, anomalies have all the characteristics that draw the attention of an analyst to further examine abnormalities in terms of flawed procedures, extraordinary occurrences that may indicate fraudulent actions and areas where a certain theory may seem invalid. A few outliers are common in large datasets and may indicate bad data but they may similarly be due to random variation or something scientifically unique.

Clustering Analysis

Clustering involves identifying datasets that are similar to each other in order to understand the similarities and differences within the data. Clusters have certain traits in common that can be utilized to improve targeting algorithms. A resulting consequence of clustering analysis can be seen in the creation of personas, which are fictional characters created to represent different types of users within a targeted demographic based on a set of behaviors and attitudes that can influence the decision of an audience to use a product, brand or site. The R programming language has a variety of functions to perform in cluster creation and is particularly relevant in clustering analysis.

Classification Analysis

This is a classic data mining technique heavily based on machine learning. It’s a systematic process for obtaining relevant information on data and metadata. Classification analysis helps to identify categories to which the different types of data belong. It’s closely linked to cluster analysis in that classification is often used to cluster data. A well-known example of classification analysis is the algorithm used to classify emails as legitimate or to be marked as spam. This is usually done based on the information within the email or the data linked to it.

Regression Analysis

In the statistical spheres, regression analysis is used to define the dependency between different variables. Fundamentally, it assumes a one-way causal effect from one variable to the other. Thus, independent variables can be affected but that does not mean that the dependency is both ways as is with correlation analysis in association. Regression analysis may show that one variable is dependent on another but not the other way around. This technique is used to determine the different levels of customer satisfaction and their effect on customer loyalty or how service levels can be influenced by factors such as weather.

Bottom Line

Data mining is an effective process that organizations and scientists can leverage to identify and select important information that can be used to create models that enable predictions on how people or systems will behave. The more data you can collect, the better the models to be employed through the aforementioned data mining techniques will be. What’s more, these techniques have proven effective in creating more business value for several different organizations.