What's Data Mining?

There are many different terms and concepts in the digital age that are often used by people who have no idea what data mining means. This is usually true right when a new, buzz-worthy technology is introduced and people want to jump on the bandwagon. They know that the earliest investors are already making millions of dollars in the field. These individuals know that they can make a little money if they jump in before the technology disappears. However, data mining is far from just another fad. In fact, it was one of the factors at the heart of the digital revolution and the earliest computers. New data mining companies and sectors are being opened on a daily basis. These companies are complex, Sophisticated, and have the potential to change how most Americans live. Knowing more about this technology can help people be better prepared for the many changes in their lives that are coming soon.

 

The Misconception of Data Mining

 

Data mining is somewhat of a misnomer for the processes that it usually describes. The term “mining” implies that an individual is attempting to bring data out of nothing. However, data mining does not involve searching for proverbial gold in some other area. It involves using big data acquired by computers and human processes in a practical way. Big data is also an ambiguous term. It primarily refers to a volume of data that cannot easily be handled by one person or a group of people. Big data does not only refer to the number of people or the individual number of data points. Royal officials handled censuses of ten million people in ancient China hundreds of years ago. The “big” aspect has to do with the number of attributes and variables involved at the same time.

 

How Mining Data and Data Science Works

 

This level of data has to be processed by computers using complex algorithms at a speed human beings cannot match. While big data has had some sort of application since computers were invented, it has only been able to be easily processed by small-scale computers for about the past decade. Bringing the computers that perform these functions into the home or into the office mean that data mining techniques can be heavily used and perfected millions of times around the world.

 

The ease of collecting and processing data means that big data has found dozens of different applications throughout the world and in many different sectors of the economy.This data can be used to make calculations, predictions, or to improve the actions and decision-making of computers. Data mining has become a catch-all term for all of these disparate uses. It can refer to data engineering, the packaging of that data for use with artificial intelligence, and the interpretation of that data for eventual public consumption.

 

Data Mining Steps

 

The first step to data mining is to actually bring in the data. This data can be acquired in a number of different ways. It can be data that is specifically gathered for the purposes of mining. A researcher or analyst can conduct tests and surveys that are specifically targeted for a particular area of data. A survey online can bring in thousands of different results in a matter of minutes.

 

Big data primarily thrives off of data that reaches a certain size and complexity. More often, the data mining operative simply uses data that is already publicly or privately. Census data is a common example of this. It is free, easily accessible, and contains thousands of data sets all across the country. In some instances, data mining operations do not need all of the data that is collected. Computers and experts use multiple processes to figure out what data is relevant and helpful.

 

Adding to a Data Mining Operation

 

There also needs to be a way in which more and more data can be added to the situation being analyzed. More relevant information can be better for a system that is studying that information and figuring out what goes right and what goes wrong. Data engineering programs and experts can then prioritize the decisions that were correct and minimize those that were mistaken for one reason or another.

 

If the task turns out to be unsuccessful, artificial intelligence analyzes all of its inputs and responses in order to determine what it did wrong. It can then store that information as more and more data that can help the algorithm improve and change behavior from its earlier mistakes. These activities sometimes happen hundreds of times per day at least.

 

Gathering data is a critical first step in any data mining process. Data must be brought into one place and then added to if at all necessary. However, there must quickly be a way to make sense of that data. This is where the tools of data mining and data engineering are critical. These tools are often attached to computer systems that can crunch large numbers in a matter of seconds. Data is fed through a group of statistical equations and algorithms which help researchers make sense of it. Standard deviations are found and then used to eliminate the impact of outliers.

 

Data Gathering and Control

 

Ranges such as dates, names, and demographic groups are processed according to their importance. Data science algorithms control certain variables and make others dependent. The statistical analysis is often presented as a jumble of numbers or a set of tables. It is up to a researcher or computer program to then further refine those numbers into data points and information sets. It is the job of the researcher to also distill the information into a simple lexicon or series of tables and charts that display the results of the big data operation.

 

Financiers of data mining operations do not want a public press release or a product description that reads like an article in an academic journal. Instead, they want to answer the series of concrete questions that eventually sparked the launch of the data mining study. Along with publishing the results of the operation, they would want other researchers either in their company or outside to be able to replicate the results and figure out whether or not they treated variables and data properly. If not, a data mining operation simply invites the application of future, more detailed studies to possibly come to a different conclusion.

 

Data Mining Example

 

One relevant application of data mining is in financial systems. The art of studying stock trading revolves around bringing in information. Fundamental analysis involves looking at large amounts of information pertaining to companies and equities and determining whether or not that information is pointing to a stock gaining or losing value over a period of time. Technical analysis involves studying stock prices over time and having that movement point to the future success or failure of the stock. Both fundamentals and technical analysis can be studied from the perspective of big data.

 

A trader could input and weigh variables such as stock price, time, and economic events. All of these variables could go into an equation that tracks how a stock performs over time and changes based on events that may pop up. Even if this variable set is hundreds of variables long, a computer will be able to process it in a span of a few milliseconds. The process is even more necessary for technical analysis. However, trading on technical analysis requires advanced statistics as it is. An inexperienced traitor can easily read charts and patterns wrong and end up losing their investment.

 

Final Thoughts on Data Mining

 

Big data is certainly a trend that is here to stay. More and more companies are investing huge swaths of money into developing more sophisticated computer and data management systems. They are teaching machines to learn with data and work alongside human beings to more accurately process it. Big data applications have the potential to revolutionize massive areas of the economy such as transportation, healthcare, and the military. It may be able to control epidemics or ensure that students get the highest quality education possible. All of the money and effort behind big data, whether it is in the financial field or another sector, means that the sector of the economy that best utilizes big data will be the one most prepared for success in the 21st century.

R-ALGO Engineering Big Data provides articles on Artificial Intelligence, Algorithms, Big Data, Data Science, and Machine Learning. Also, R-ALGO Engineering Big Data provides R Tutorials on how to implement Machine Learning Algorithms with provided datasets.

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