Tools and Techniques of Data Mining

Tools and Techniques of Data Mining

Published On April 22, 2014 -   by

As more and more information is generated in modern business transactions ”“ and new kinds of information are identified that were previously unavailable, such as metadata ”“ the importance of Data Mining grows and grows. Luckily, there are a large number of data mining tools and techniques available to data mining professionals and the organizations they work for, allowing for a wide variety of basic tools to be installed and used in a variety of ways. This allows a fine-tuned approach to interpreting and manipulating data to give as detailed a picture of the past, present and future of any body of trending data. Here are some of the common tools you’ll encounter in the field of data mining, and the techniques that can be applied to get the most out of your information.

Database Analysis

Traditional data mining tools and techniques work with existing databases stored on enterprise servers or even local hard drives. They interpret the data stored there using pre-defined algorithms and queries written out in a database-specific programming language (macros) to reveal patterns in the data that would otherwise be invisible.

One of the main techniques used with these traditional data mining tools is to analyze large amounts of data that is already sorted into broad categories in order to see sub-patterns. For example, a database of sales figures can easily display monthly sales trends simply by accessing the database’s built-in query and table system. A data mining tool installed to the server can then analyze those broad numbers to identify aspects affecting monthly sales that are not immediately apparent, and, most importantly, render that analysis into an easily-readable report form that makes those patterns explicit.


A more recent innovation in the world of data mining tools and techniques is the Dashboard. A data-mining Dashboard is a piece of software that sits on an end-user’s desktop or tablet and reports real-time fluctuations in data as it flows into the database and is manipulated or sorted. Typically, historical data can also be accessed via the Dashboard, although the data mining of historical data is not as nuanced as that available in a traditional data mining tool.

Dashboards are typically used by managers and other positions to track the effect of events and other influences on data streams in real time. One example is monitoring new picking policies in a warehouse as a company attempts to massage their logistical management of stock ”“ a Dashboard allows the company to see the effect of new policies immediately, quickly analyzing just a few hours of data to see if they getting the desired efficiency or not.

Text Analysis

One of the newer innovations in data mining tools and techniques are text-mining applications. These tools take disparate forms of textual data ”“ word processing documents, plain text files, ‘flat’ text formats like PDF files or presentation files ”“ and mine them for patterns in the text. This allows companies and users to use data mining tools and techniques without having to open each document in a separate application or perform cumbersome (and error-introducing) conversions on documents.

Text analysis has many possible techniques and applications. One popular one involves seeking out plagiarized or ‘copy pasted’ content. Text analysis data mining tools allow users to quickly scan huge amounts of text in different formats to identify identical strings and report back the odds that a particular piece of text was lifted from an existing text. Universities and colleges are using such tools more and more commonly to fight plagiarism in classrooms.

Increasingly, data mining tools and techniques are absolute essentials even for businesses that traditionally had no use for them, and the tool and techniques keep evolving to keep pace with modern innovations.

– Data Czar @ DEO

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