What is data mining? Analysis methods for big data

The term “data mining” refers to the targeted analysis of large datasets to uncover new, potentially valuable information. We’ll explain the term in more detail and outline relevant analytical methods.

What is data mining?

Data mining is the process of transforming data into meaningful insights by employing specialized tools to extract relevant information. But why is it called data mining? To better understand what data mining means, it’s helpful to first break down the metaphor. Let’s take, for example, online tracking tools. These are everywhere, gathering an overwhelming amount of data from visitors. While the data at first may seem useless, with data mining, it’s possible to extract meaningful information from these mountains of data. Unlike traditional mining, data mining uses statistical methods to uncover patterns, trends and relationships.

Data mining is typically discussed in the context of big data. This refers to data sets so vast that they can no longer be processed manually, requiring computer-assisted analysis. Data mining methods can, in principle, be applied to data of any scale. The insights derived from data mining can inform the strategic direction of online business and guide marketing decisions. As a result, data mining has a wide range of applications.

Applications of data mining

Data mining offers the possibility to optimize e-commerce using a scientific approach. Here, large data sets build the basis for explanations and prognoses. Statistically processed and clearly visualized, they allow online store owners to identify factors for a successful online business and to model their online store marketing strategies. Data mining is used in this process to:

• Divide markets into segments
• Analyze shopping cart data
• Create consumer profiles
• Calculate product prices
• Set up prognoses on contract periods
• Analyze demand
• Identify errors in the purchasing process

How does data mining work?

Data mining is part of the Knowledge Discovery in Databases (KDD) process, which includes the following steps:

• Define objectives: First, specific questions that the data analysis aims to answer need to be established. This helps to identify relevant data and suitable analysis methods more effectively.
• Data preprocessing: The quality of the information derived from data mining depends heavily on the quality of the data foundation. Relevant data should be cleaned before analysis to remove duplicates, outliers and other distortions. It may also be necessary to convert the cleaned data into the format required by the analysis method.
• Data analysis: This is the stage where the actual mathematical data analysis takes place. The analysis techniques used here depend heavily on the defined objectives and the characteristics of the data. Both traditional data analysis algorithms and newer algorithms based on neural networks and deep learning can be applied.
• Interpretation of results: Finally, the results of the analysis are evaluated. If the results are clear and insightful, they may reveal new correlations and provide insights that can influence future business strategies.

Data mining methods

Many methods have been developed to identify important relationships, patterns and trends in data, enabling the extraction of valuable business insights from large data sets. These methods can also be used for statistical processes.

• Outlier detection: Extreme values that stand out from the rest of data are known as outliers. In data mining, outlier detection is used to identify atypical data sets. In practice, these data mining methods can, for example, reveal credit card fraud by exposing suspicious transactions.
• Cluster analysis: A cluster refers to a grouping of objects based on similarity relationships among the group members. The goal of this analytical method is to segment unstructured data. To achieve this, algorithms like K-Nearest Neighbor (KNN) are used, which search through large data sets for similarity patterns to identify new clusters. If a data set cannot be assigned to any cluster, it can be interpreted as an outlier. A classic use case for cluster analysis is identifying visitor groups.
• Classification: While cluster analysis primarily focuses on identifying new groups, classification uses predefined categories. Data points are placed into categories by matching their traits with other data points in the dataset. A decision tree is a common method for automatically classifying data. For each node, a characteristic of the object is evaluated, and its presence or absence determines which node is chosen next. This process can be used in e-commerce to divide customers into different segments.
• Association analysis: Association analysis seeks to uncover relationships within datasets that can be expressed as inference rules. In e-commerce, this data mining approach can reveal correlations between products in shopping carts, with patterns like “if product A is purchased, product B is likely to be purchased as well.”
• Regression analysis: Regression analyses help create models that explain dependent variables through various independent variables. In practice, this means that the prognosis for a product’s sales performance can be created by correlating the product price and the average customer income level in a regression model.

What are the limits of data mining?

In data mining, statistical procedures are employed that make it possible to carry out a fundamentally objective analysis of available data sets. The rather subjective nature of selecting an analysis method as well as the various algorithms and parameters can, however, lead to distorted results, regardless of one’s intentions. Such effects can be avoided by outsourcing data mining processes to external service providers.

Finally, it’s important to note that data mining only offers results in the form of patterns and cross-connections. Answers can only first be obtained when the analysis results are interpreted with regards to previous questions and goals.