What is Visual Data Mining?

Whether you work as a data analyst, run a data-centric startup or want to learn more about data mining as a whole, visual data mining can be an interesting place to start. According to Finances Online, 53% of companies still rely on their CFO for data analytics without delving into specialized software or data mining, while studies show that more than 25% of data will be generated in real-time by 2025, making data visualization and analysis crucial.

Coupled with a reliable writing service that specializes in content creation and editing, visual data mining can become a beneficial part of your business model or professional skill set depending on the scale at which you want to learn about it. With that said, let’s delve into what makes visual data mining tick, its main benefits, viable data types as well as several useful tools to consider exploring in regards to data mining.

Data Visualization

Visual Data Mining Basics

Let’s start off by discussing the term “visual data mining” in greater detail before we move on to its functionality. Visual data mining (or VDM) is a process of interaction and analysis of visualized data with the goal of gaining a deeper understanding of certain data. The data processed via VDM can be numeric, empiric or based on a plethora of filters and categories within a document. For example, if you insert numeric data in regards to your business’ finances for the past few years into a VDM tool, its interface would present you with a web of interactive lines, patterns and visual elements available for visual mining and analysis.

VDM utilizes specialized tools that carefully analyze data clusters inserted into their algorithms and present data analysts with visualized data for further exploration. This can help analysts discover patterns between different empiric values or help them make more informed business or development-related decisions. We often see VDM used in weather pattern platforms such as Lightning Maps which uses visualized data to present a set of empiric values to a wide range of viewers unfamiliar with raw information analysis. It is a highly useful albeit niche data mining process that can be used by data analysts and managers of various skill sets with some time and effort.

Related: Data Processing CycleImportance of Data Processing, Data Processing Cycle

Benefits of Visual Data Mining

Now that we have a clearer understanding of what VDM is, let’s talk about its advantages and why you should use it. According to Tech Jury, 90% of all data available globally was generated in the past two years, while 97.2% of organizations actively invest in Big Data and AI in hopes of gaining the upper hand with their data processing operations.

Whether you look at these numbers from the perspective of a CEO or a data analyst looking for his or her next career choice, it’s clear that visual data mining and data management as a whole is a sought-after profession. Learning how to utilize VDM efficiently and knowing how to extrapolate useful data from its visualized patterns can bring a plethora of benefits to you, including the following:

  • Intuitive, visualization-based exploration of raw data
  • Faster, more informed decision-making
  • Easier filtering of noisy and tangled data repositories
  • Better finance and resource management
  • Improved productivity and lowered downtime
  • Improved identification of bottlenecks and opportunities

Viable Data Types for Visualization

While VDM can be highly beneficial for you both as a business and as an aspiring data analyst, certain rules and processes still need to be followed. For one, not every type of data will be viable for visual data mining and each data type will be visualized in a different manner depending on its complexity and your target outcome. Typically, VDM follows a three-step process of “Overview, zoom and filter”.

While these steps are self-explanatory, it’s worth noting that the first step will allow you to gain a wide overview of all of your inserted data. Zooming in on different data clusters within visualized data will then allow you to filter out different values and create patterns before you come to the conclusion you were looking for. In order to do that efficiently, you should be aware of the viable data types open to VDM analysis going forward:

  • One-dimensional data such as time, money or resource
  • Two-dimensional data akin to geographic maps
  • Multi-dimensional data which relies on tables and charts
  • Text and web-based data which relies on written online content
  • Hierarchical and graphical data which also relies on web-published content
  • Algorithms and code which is used for debugging and software updates

Related: Data Management Best PracticesInformation Processing CycleData Visualization

Visual Data Mining Tools to Consider

Now that we’ve covered the basic principles, benefits and rules of visual data mining, let’s turn our attention to several tools that specialize in VDM. While there are no bad choices here, these tools vary in complexity, depth and resource requirements and should be tested to see whether they fit your analysis style and needs or not.

Orange – This is a Python-based visual data mining tool that relies on open-source code and machine learning for its visualization. Orange features a plethora of add-ons and mining features that can be customized for specific VDM tasks depending on your needs. It is a good choice for both data analysis novices and VDM experts due to its community-based development and user-friendly nature.

Weka – Weka is a machine-learning reliant data mining platform with a wide range of visualization, clustering and data-processing features available. It is based on Java and is quite sophisticated in its range of data mining options, including VDM, data experimentation and processing to name a few. While it may be overwhelming for novices, Weka is a good choice for seasoned mining specialists given its volume of machine learning algorithms available.

NLTK – While it may not be a lot to look at, NLTK is an in-depth data mining tool based on Python. It is highly customizable as it provides users with a pool of language processing tools that cover a variety of mining tasks, data scraping capability and machine learning to boot. The graphical user interface (GUI) of NLTK may be minimalistic, but it’s processing power, VDM utility and a list of features more than the cover for its lack of visual fidelity.

In Conclusion

While it may seem overwhelming at first, VDM can provide you with a unique look at how data works more than any other form of text-based mining would. Visual data mining represents a specific sub-set of data analysis which can offer visualized information for further research and extrapolation.

Whether for personal development or business management purposes, learning more about visual data mining can only benefit you in the long run. Don’t be turned off by its learning curve and try to come up with unique ways in which you can use the technology both for your own and your employer’s benefit. Once you do so, utilizing VDM will be no different than writing an essay or creating a spreadsheet.

Related: Data Processing & Data Processing Methods, Data presentation and analysis