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What are Data Mining Algorithms

What are Data Mining Algorithms and Their Major Types

Data Mining algorithms are the new buzzwords, when it comes to corporate market analysis and scientific research projects, that need to analyze gargantuan amounts of data. This article elucidates what these algorithms are and how they can help businesses and research projects.
Techspirited Staff
Last Updated: Feb 8, 2018
Algorithms for data mining are tools that help in unearthing useful relationships and patterns from any huge body of data. In this article, you will find a basic overview of what it is all about.

What is Data Mining?

The term data mining has been coined very wisely and the name itself is self-explanatory, if you look deeper into the mining analogy. Real world mining is digging through tons of dirt and rubble, to find useful minerals from the Earth. Similarly, data mining is digging or extracting useful information and predictive patterns out of a flood of data. Be it any business or human activity, to plan a winning strategy, one needs to analyze the available raw data. Most companies deal with huge quantities of customer survey data or data related to company performance.

To survive in today's competitive business world, one needs specialized knowledge, which can help you in coming up with the right business strategies, adapted to customer needs. Software programs based on these algorithms can identify patterns within huge data clusters and extract interrelationships among different categories of data.

This can help in predicting future patterns of market trends and give a company, an edge over competitors. Stock market data is mined for predictive patterns, which can help in making future predictions. The Google search engine uses such an algorithm, to select the most relevant web pages, out of the millions available on the Internet.

When it comes to scientific research projects, analyzing experimental data is important to validate scientific hypotheses, for which experiments are designed. One example of a scientific endeavor that is currently using mining algorithms extensively is CERN's 'Large Hadron Collider' machine, which generates several terabytes of data everyday. Analysis of that data is automated through ingeniously designed mining algorithms. The applications of these algorithms are universal, as data abounds everywhere.

Types of Algorithms

Just as to mine minerals, one needs to use the right tools that can penetrate Earth and access the minerals, one needs an intelligently designed data mining algorithm, that is suited to the kind of data one is dealing with. Data can be of various types like numerical, alphabetical, fact-based, and a complex amalgamation of all these. There are a variety of software programs based on various mining techniques, that one can choose from. There are various approaches of sorting data and mining it for extracting interrelationships. Here are the two major types of these algorithms.

Classic Algorithms
The first class of algorithms are the ones which use statistical techniques and plain old common sense for analyzing data. One primary method is regression. In this method, given data is plotted and a mathematical curve that fits that data is found. Consequently, the equation of the curve is used to predict future course of data flow. Regression is modeling data through the use of mathematical equations and there are various types of regression techniques used in data mining.

Other than regression, two of the oldest algorithms are 'Clustering' and 'Nearest Neighbor'. They are quite similar in principle. Mining algorithms based on clustering use the principle of bunching like things together into clusters of uniform data. It is like a taxonomy scheme. The 'Nearest Neighbor' algorithm can predict future data course, by comparing it with the older data which is most similar to it. Prediction is done on the basis of the idea that, what happens with the nearest neighbor of a data stream, could happen again with the new stream.

Modern Mining Algorithms
Modern algorithms are mostly based on the concept of decision trees. That is, data is sorted on the basis of questions, that sift it into a logical framework. The algorithms are based on the idea of capturing most types of data, using a complex decision tree framework. It is a net of logic that captures every small piece of data. It enables one to predict data flow from a given framework. There are many types of such algorithms, which are a modification of this central idea.

Select a software based on an algorithm, that is best suited for your business area, according to the type of data you need to handle. Many commercial database software programs come with inbuilt mining tools, that you can use for mining useful knowledge, out of the data.