Data is among the most valuable resources for any company and entrepreneur out there. The data you generate, collect, and the process can define your business in the best possible ways and serve as its main driver if treated properly. In particular, with all the insightful data you get on a regular basis, you must know how to get to the bottom of it and literally extract business-boosting insights. Of which there are plenty — enough for valuable forecasts and pinpointed business optimization efforts.
This is where data mining comes in as a way to dive into the accumulated data assets and get all the useful stuff out of it. This can be done with the help of various data mining techniques that data scientists have been elaborating and introducing for years. And this is what we are taking a look at in this article — the most must-know types of data mining techniques that can help you discover new business horizons and expand in the most unexpected ways.
A data mining technique is a specific approach to turning terabytes of raw data into business-valuable insights. This may involve preparing and processing data in various ways. Advanced data analytics can be achieved through the savvy application of math and statistics. Based on different mathematical and statistical approaches, different data mining models can be created.
To go sufficiently in-depth and bring fruitful results in reasonable terms, advanced technologies are used for major data mining techniques — Machine Learning (ML), Artificial Intelligence (AI), Big Data, automated statistical data harvesting, etc. With such advanced powers, today’s field of application of data mining includes not only marketing and business optimization purposes but also data mining techniques for fraud detection, forecasting various conditions, and statistical indicators.
The goal of the following data mining approaches and concepts is to help data scientists examine potentially insightful data patterns that are meshed inside extensive volumes of source data. Some of the most useful statistical data pieces can be extracted from the depths of long-standing databases. The major theme of this article is that different data mining techniques may fit different underlying purposes and bring different results in matching circumstances. Others may also reinforce each other’s effect when combined.
So these are the 14 top relevant and effective methods right now in our own data specialists’ opinion described to give you a better idea of what exactly may fit your data mining needs.
This is the kick-off stage for data preparation. This is, basically, where we take chunks of raw data to cleanse and format it in such a way so it can be used as the input asset in various data mining techniques and algorithms. The collecting of data from the required available sources is not only about obtaining pieces of data — this is where specialists also select, format, transform, clean, and anonymize it.
To handle all that, the whole process may include a bunch of sub-tasks:
The main goal here is to outline the basic attributes of the data we are working with and indicate its major use in the ongoing data mining. In the long run, you can achieve proper output data quality, governance, and comprehensive organization and structuring with thorough preparation.
“Fun fact” — data cleaning and preparation make up 90% of your whole data mining effort. Thankfully, advanced, powerful software tools and business intelligence services can make a data scientist’s life much easier.
Another essential part of raw data organization before employing data mining concepts and techniques is its thorough classification. This is quite a complex task where a data specialist must outline the proper classes and subdivide all available information into them. Different types of classes can be defined for different business specifics and data mining purposes, for instance:
You name it — these can vary a lot based on your data mining goals and specifics. Timely data classification also comes in important when you extract sensitive or secret corporate information that should be well-protected or restrained from disclosure in commonly accessed documents or reports.
When handling data mining techniques and algorithms, a data scientist comes across chunks of data and extensive datasets where numerous features or attributes may be clustered. This is called high-dimensional data, i.e., data that consists of many valuable layers and bits. Clustering helps actually fit all the bits and odds into comprehensive clusters holding similar data points.
For this, some complex math comes in, and Euclidean, Jaccard, Cosine, Edit, or Hamming distance measures are used to identify the distance between similar and differing points. Thus, related points are closely clustered together while differing ones are set apart in separate clusters. The resulting clusters can be conveniently assessed visually by specialists.
Different clusters with specific purposes can be tagged and colored for proper data types distribution. An even more consistent picture can be achieved with graphs. In the long run, you can, for instance, cluster different customer groups and identify similar and dissimilar points between them better segment your customer base and get to know your customer closer as a whole.
The essential data mining techniques include association. This is where statistical data is analyzed to find interlinked data and data-driven events. Correlations and relationships between such events can point out valuable marketing information, e.g., the best product recommendations that usually accompany specific product purchases (like recommending to buy a phone case for a newly-purchased smartphone).
Regression serves as the ultimate tool for seeing how certain variables interact with each other to predict new variables that can be spawned by the “joint effort” of the two previously existing ones. In more technical terms, it enables you to forecast the value a certain data entity may acquire in the future.
This is where the power of AI comes in as special neural networks can be created for linear and non-linear regression that resembles the way the human brain links up events both objectively and intuitively. This opens vast horizons for, e.g., smart personalization in eCommerce, fraud management in finance, cyberattack detection in security, and predictive analytics in healthcare, where complex diagnostics can be carried out by intelligent regressive tools.
To give this one a comprehensive data mining techniques classification — outliers are anomalous bits of data within datasets. Analyzing such outliers, you may identify out-of-line occurrences in clusters and sets of data. In marketing, this helps identify and analyze sales spikes and drops in the midst of the season, odd target audience members, and similar occurrences.
And the main thing is that by digging into such insights, specialists can understand the reasons for them occurring in the first place and prepare for similar happenings in the future. Intrusion, fault, and fraud detection can also be tackled and reinforced with the help of this data mining classical technique.
Here’s how this approach works — a specialist specifies a certain period and sets sequencing smart tools (combination of ML and AI) to outline regularities, similar models, and other tendencies in data strands. For example, sequencing helps group types of products that are best sold together during specific seasons. This is done by indicating particular tendencies that take place in sequence, i.e., one after another.
A very commonly used technique, mostly due to its simple nature. A “tree of decisions” is built in order to find the most probable answer to the question put in the “root” of this “tree.” Thus, we can reach comprehensive descriptions of outputs coming from certain inputs. This is one of the major big data mining techniques that manifests in random forests — sets of decision trees that can be combined for more broad predictions and precise results.
With enough input data timely mined from databases, one may predict, for instance, the seasonal demand for certain goods. If we take the most relevant example right now, you may as well predict the demand for life insurance services based on the seasonal illness statistics and COVID-19 spike predictions.
Storing data in specialized warehouse environments simplifies the life of a data scientist/analyst and helps put the collected data in all the right places for further underlying tasks. In particular, structured data can be conveniently stored in a relational DBMS (database management system) rich with BI processing, data management, reporting, and other handy tools.
Your data warehouse can be a cloud environment (the most common and reasonable choice) or proprietary or semi-proprietary space (still common, but gradually outdated approach due to higher expenses and more intense maintenance efforts).
This is a data warehousing counterpart where data can be stored and analyzed within a DBMS in a similar way, yet you may store both structured and unstructured, relational and non-relational data “lakes” for more generalized analysis and processing. In simpler words, this means that you may store and process-relational internal business data and non-relational data coming from, e.g., IoT and connected mobile devices in one place.
There are several ways to gain insightful data-driven predictions. Data mining classical techniques allow predicting various potential/probable patterns and tendencies based on the combination of historical and existing data (statistical exclusions and decision trees may help here). This same basic principle can be reinforced and automated with the help of AI and ML tools. Efficient prediction of market trends is one of the most sought-after capabilities across a myriad of commercial, government, and even non-profit industries.
Dynamic data streams (which are different in nature from regular unstructured or structured sets of pre-collected data) must be timely stored and processed as well. Otherwise, you are risking losing invaluable pieces of info for good. However, specialists face a major challenge of processing very rapid, continuous (basically, infinite) data streams. This is where a specialized data stream management system must be employed.
Frankly speaking, the specifics of handling intense data streams deserve a whole separate article of its own. To put it briefly, data streams must be timely and properly:
For storage purposes, a specialist may elaborate an archival store where big sets of data streams can be archived or a working store where streams are placed only partially or their summaries are stored instead. This depends on a variety of project-specific aspects.
A stream’s set of key attributes must be identified for gaining proper samples that can be processed by different class queries.
Bloom Filtering is a technique that helps systems filter out odd data entries, letting through only the required types of datasets.
Lastly, in order to identify distinct elements in universal data streams for their further classification, the Flajolet-Martin technique can be used. It helps to hash elements to integers. As a result, we get sets of binary numbers and with a bunch of math formulas acquire efficient data-driven estimates.
Specialized neural networks may employ complex statistics to recognize difficult-to-identify elements and reinforce your statistical data mining techniques all-around. AI can help your system acquire great static analysis capabilities along with self-learning capacities of getting better with time provided by machine learning. And the involvement of deep learning allows for more intuitive data-driven system decisions and outcomes.
Last but not least, there is more value in your archived historical warehouse-stored data than you might think. And this is where long-term memory processing helps efficiently extract it. This approach implies the complex analysis of data over extended periods. The available historical data may serve as a great input here. In the long run, it helps identify subtle time-based data patterns. It is good for cutting extra expenses and optimizing costs.
Above, we tried to follow up data mining techniques with examples. However, you may be interested in some real-life instances of applying some of those approaches that took place. Let’s take a look at some examples by industry:
NIX United is a seasoned provider of data analytics services with an extensive pool of specialists savvy in all the above-mentioned data mining techniques and more. We know how to mine data in the most individually-beneficial way. No matter which industry you are operating in and which goals you tackle, we have the experience and expertise to tailor the data science team structure and tackle projects of any scope and complexity.
The ultimate benefit of employing data mining for data-driven growth and improvement cannot be overrated. You can see that both from the opportunities data mining tools grant and real-life examples of their use taking place in the world right now.
You may freely use this article as your main classification of data mining techniques. It may also help you in an individual comparison of data mining techniques to find the most fitting approach for your specific goals. And if you have any further questions or project ideas — contact us for specialized assistance!
A data mining technique is an approach to extracting valuable insights from raw chunks of accumulated data. These insights, in turn, can help gain more insightful knowledge, forecast trends, and optimize business in line with identified tendencies and patterns.
There are several major data mining techniques, the most relevant of which we mention in the article. But if we had to outline the 5 approaches without you which it is impossible to efficiently mine data, these would be:
Yes, SQL capabilities make up an efficient data mining approach for extracting data and setting off predictive analytics across servers. There are a myriad of related tools that can be used for various data mining tasks (e.g., preparation, classification, reporting, smart analytics, etc.). However, this is a narrow-focused approach for server environments that not every project needs.
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