# How To Analyze Big Data With Data Mining Techniques

In this article we will focus on the analysis of large volume or how to analyze large data sets. We will see why companies can make use of these large data sets to improve their efficiency, and what limitations they have. It is also possible to apply the techniques of large data sets to other domains such as health care, manufacturing etc. In the end, the key question is: how to analyze large data sets?

Before analyzing large data sets, it is important to demarcate the domain in which to work. The domain must have some special properties such as high relevance to the subject. As an example, if the main topic of discussion is manufacturing, there are different categories such as electronic parts, garments, etc. The large amount of data that is available today already satisfies this requirement. Hence, the airline data set used above serve only to illustrate that can do the usual statistical analyses quite well, and helps to compare (at least part of) with the analyzed data from the SAM method.

It is important for the user to understand that the data sets can be analyzed by a machine even though it is not programmed to do so. There are tools for this which can be downloaded online, and there are also tools available for personnel to analyze the data sets by themselves. Analyzing large data sets is something of a science in itself, and it is not possible for one person to analyze it in a complete way. Hence, the importance of the knowledge base and its regular updating is very much felt in the domain of Hadoop usage.

We have seen how to analyze big data using traditional statistical analysis techniques like the normal curve, the binomial tree, the logistic function, etc. The domain of big data consists of other techniques as well as supervised learning, decision trees, greedy high optimizers, etc., and these techniques have their own limitations as well as advantages when used. The problem is to keep track of all the relevant data sets and their inter-relationships when trying to make inferences and generalize from the data set. The main limitation of this approach is that one cannot make generalizations across all data sets and attributes.

There are some specific tools for this, which are gaining in popularity as how to analyze big data grows in use. One of the tools is the data mining technique, which is based on the search for patterns in large sets of unprocessed data. Often the pattern can be extracted from the data set by applying some simple mathematical algorithm. This method has been effectively used for decades now to find oil and natural gas exploration results, stock market signals, financial market trends, and the likes.

Some of these problems can be much more complicated and so it is quite imperative to use data mining techniques only for highly complex problem solving situations. Examples include the extraction of financial information from large retail trades or bank statements. Also, the data mining technique can not generally be used for gathering data on the Internet or collecting demographic information. The basic algorithm for data mining involves sorting large databases into smaller ones, grouping similar sets together, and then analyzing the relationships among them. Sometimes it is even necessary to apply more than one algorithm at a time in order to extract the best results from the data sets.

It is possible to train computers to solve such problems by means of a combination of algorithms and data mining techniques. The challenge however is that the quality of the solution tends to vary depending upon the developer of the software. Most people who are developing such software programs do not have strong mathematical backgrounds. Hence, while some of them might be efficient at handling simple problems of how to analyze big data, others will not be efficient at handling complex problems involving hundreds or thousands of variables. In addition, most developers tend to select a particular algorithm that works well with their programming language and platform.

On the other hand, organizations that require advanced analysis techniques may require the services of highly trained personnel who possess the requisite mathematical skills along with programming experience. Hiring such expert professionals would be a very expensive and inefficient approach as this method of data collection is expensive and inefficient in nature. However, organizations can choose to work with vendors who offer data mining solutions which are based on both industrial and business intelligence data requirements.