Data Mining Techniques (Part 2)
Welcome back to the next part of our article on Data Mining Techniques, where we focus on how Business Intelligence (BI) uses data mining techniques to make sense and use large volumes of data.
In the previous part, we discussed some common and basic data mining techniques, which you can check out by clicking here. This part will discuss some more advanced data mining techniques and processes like sequential patterns, neural networks, machine learning, and Artificial Intelligence (AI).
Let’s continue.
Data Mining Techniques (Part 2)
Here are some of the more advanced data mining techniques businesses use in their BI efforts to turn raw data into meaningful and valuable information.
- Sequential Patterns Technique
This data mining technique aims to discover a series of events in the data that take place in a sequence. It is particularly valuable for data mining of data like transactional information.
For example, sequential patterns can help a retail business discover what shirts their customers are more likely to buy after an initial purchase of a pair of slacks. They can then market those shirts to customers who purchase those slacks.
Sequential patterns are great for businesses who want to “up-sell” or “cross-sell” products by recommending additional items to customers, resulting in increased sales.
- Statistical Techniques
Statistical techniques are crucial data mining techniques because they are at the core of most analytics of the data mining process. Various analytics models of data mining are based on the statistical theory that produces numerical values applicable to particular business objectives.
For example, neural networks of image recognition systems use complicated statistics to identify whether a picture displays an apple or an orange. Statistical models are so crucial that they represent one of the two main branches of AI.
Models for statistical techniques can be static or dynamic to get better over time when machine learning is involved. Statistical data mining techniques are vital for analytics, and without them, various data mining processes would not be possible.
- Decision Trees
Technically, decision trees are not exactly a data mining technique; instead, they are a machine learning technique and a specific type of predictive model that allows businesses to execute data mining effectively.
It is a well-known white box machine learning technique, which means it clearly explains behavior, process, how it produces predictions, and its influencing variables. Its uncomplicated and clear-cut nature makes it a white box machine learning technique.
Businesses can clearly understand how the data inputs affect the results with decision trees. Companies can create predictive analytics models by combining various decision tree models, commonly known as a random forest.
Unlike a decision tree, complex random forest models are considered black-box machine learning techniques because businesses don’t always understand their results based on their data inputs.
However, such a basic form of ensemble modeling is typically more accurate than using decisions. These models help businesses make fairly accurate predictions using data and insights into what may unfold in the future, improving their decision-making.
- Data Visualization Techniques
Visualizations are key for making sense of data, more specifically analytical data. They provide businesses with crucial insights into data based on sensory perceptions that are easy to understand. These days, data visualization has improved, and we now see dynamic real-time visuals.
They are typically color-coded for characterization to reveal various trends and patterns in the data. Data visualization is most commonly used in Business Intelligence, data analytic, and data mining dashboards to reveal data mining insights.
Businesses use these dashboards and their visualizations to highlight patterns in the data instead of simply using the numerical results of statistical models. This makes data more straightforward to read and understand, thus promoting faster, real-time decision-making.
- Data Warehousing Techniques
Data warehousing is a crucial part of BI and data mining. A data warehouse is where useful; structured data is stored, secured, managed, and accessed when needed. It is a database management system where data can be used for data analytics, reporting, and basic dashboard purposes for BI.
These days, we have cloud-based data warehouses for better storage, security, management, and easy access. Since data warehousing requires structured data, data mining techniques are used to turn unstructured data into structured data for warehousing.
Traditionally, data warehouses were used for historical data, where businesses could store their historical data from various sources in a structured manner. However, modern approaches offer greater flexibility resulting in more in-depth, real-time data analytics and users.
- Machine Learning and Artificial Intelligence (AI) Techniques
Machine learning and artificial intelligence (AI) are the more advanced parts of BI, and they represent the most cutting-edge developments and techniques in data mining. Things like deep learning from machine learning can provide highly accurate predictions from large datasets.
Such processes require massive amounts of data and are typically used at scale. They can be incredibly useful for mining and processing data for AI technologies like speech recognition, image recognition, or complex text analytics using Natural Language Processing.
Such data mining techniques are great for deriving value from large sets of unstructured or semi-structured data, which would otherwise be improbable.
Conclusion
Businesses today utilize many of the mentioned data mining techniques and more for their Business Intelligence objectives, processes, and tools. From statistical techniques to complex random forest models, machine learning, and AI, there are a lot of useful data mining techniques, processes, and tools to work with.
With such a wide range of techniques for data mining, it is essential to have and use appropriate tools to optimize your data analytics and BI. Your business needs the right cloud solutions, professionals, and tools for implementing effective data mining and BI.
There are solutions for all of this, with all the required features, but it is crucial to find the ones that fit your business needs, which is why we highly recommend deltAlyz for the best data mining and BI solutions.
You can learn more about data mining techniques, Business Intelligence, data analytics, machine learning, Artificial Intelligence, and how to deploy these technologies for your business by visiting our website today.
