Different Data Mining techniques are used for different problems in corporations and hence, we can say that the overall knowledge of the business problem helps in determining the technique for data mining that is likely to generate the best results for the corporation.
In the contemporary world of globalization and digitization, we are largely surrounded by mammoth sets of Big Data which is expected to increase 40% every year for the next decade. The irony of the matter is that despite of such a large magnitude of data availability, the world still starves for beneficial knowledge. Why is this the case? It is important to understand that the data is buried inside large chunks of big data and can only be extracted through significant techniques.
In this small piece of literature, we shall provide you with the 3 major techniques that are crucial for data mining.
In order to retrieve relevant, productive, and beneficial information about data or Meta data, this analysis comes very handy. The classification analysis is used for the classification of different data into classes or segments. This process is similar to clustering as the process of segmentation is carried out in both the techniques, but the uniqueness of Classification analysis is that, unlike clustering, the analysts would be having the overall knowledge and understanding of the different classes or segments. Hence, we can say that in classification analysis different algorithms are applied in order to decide how the classification of new data should be done. For example, the Microsoft Outlook uses a particular algorithm to segment an email as Spam or Legitimate email.
The Regression Analysis, in the statistical terms, refers to the process of analyzing as well as identifying the connection among different variables. This analysis is important because it helps you to gain a sound understanding of the characteristic value of the changes in dependent variable, if the independent variable changes at any time.
The clustering analysis is another important technique that uses clusters comprising of objects which are similar in nature. This means that with in the same groups the present objects are similar to each other and different from the ones in the other groups or the so-called clusters. Therefore, we can say that the clustering analysis is the systemic process through which we can explore clusters in the Big Data, in a manner that the overall degree of association between the two sets of data is the highest if they are from the same group and lowest otherwise. For instance, this Clustering Analysis is used in Customer Profiling.