Data Mining

Introduction

In today’s informative world, data are produce enormously every now and then. It could be in scenarios like marketing, manufacturing, transactional, defence, telecom, actuarial, service and so forth. The process of extracting useful information from large sets of raw data is known as Data Mining. It uses descriptive and inferential statistical analysis for analyzing the extracted data. It would be helpful in business decision making processes.

Data Mining Techniques

      • Classification – It is one of the important data mining techniques which classify or categorize the large set of data in a useful manner. This method helps to classify data in different classes. It is discrete and doesn’t imply any form of order. For example, the Credit Card Company would able to provide credit based on credit score. It would be high, average and low. In data mining, classification models such as decision tree, logistic regression, etc. are used.
      • Clustering – Classification helps to classify data into different classes. Clustering is a technique to cluster or subgroup the classified data into a similar format (based on characteristics or properties). For example, the Credit Card Company would able to provide credit based on credit score. It would be high, average and low. These classes (high, average and low) can be a cluster in a format like the private sector or public sector employees, male or female, etc. Some of the clustering methods are a hierarchical, partition and so forth.
      • Association – It helps to find the relationship between two or more data variables. We can solve some of the questions like – “What is the association between the climate and sales of a cloth?” “How strong is the relationship between the investment and sales of a company?” Correlation analysis is used to find the association between the variables in data mining. Correlation methods are Pearson’s product-moment correlation coefficient, Kendall and Spearman rank correlations, etc.
      • Prediction – Here, on this method, we predict some of the future outcomes based on past data. Suppose after knowing, “How strong is the relationship between the investment and sales of a company?” We could predict sales based on investment data by using Regression Analysis. Based on this example, we could say that it is very helpful in market analysis and so on. It uses the other data mining techniques like classification, clustering and association to predict the outcome of a data.
Application of Data Mining in Finance

Statistical methods used in Data Mining

  1. Sampling – It is a process of taking a small set of observations (sample) from a large population. It is a common tool used in any type of data analysis. Some of the sampling methods are random sampling, stratified sampling and cluster sampling. As data mining we know that it is an extraction of information from a large set of raw data. But sometime due to time constraints or it could be similarities in data – we could not analyze the whole data. So in such circumstance, we can go for sampling.
  2. Correlation Analysis – It is used to study the closeness of the relationship between two or more variables i.e. the degree to which the variables are associated with each other. Suppose in a manufacturing firm, they want the relation between –
        • Demand & supply of commodities.
        • Production volume & the efficiency of machinery equipment.
  3. Regression Analysis – It is a commendable statistical technique used in data mining. It helps to predict the value of future outcomes by using the past data. This method helps in forecasting the data and Time-series Analysis. The different types of regression analysis are
        • Linear Regression
        • Multiple Regression
        • Logistic Regression
        • Poisson Regression
  4. Graphical Analysis – Here, the data are presented in the form of graphs or diagrams. When we presented data through diagrams and graphs – it looks more convincing & appealing. Thus provide the meaningful outlook of a data. Some of the popular graphical tools used in data mining are

What role does Statistics play in Data Mining?

Statistics is the foot of all data mining techniques. There are various statistical methods which are being used in data mining techniques. Some of them are sampling, correlation analysis, regression analysis and graphical analysis. Thus statistics provides a various techniques to analyze the large forms of data. For the applications of these techniques, one should have a basic knowledge about it – “What are these methods mean?” “Where and when we can apply?” and so forth. Hence statistics plays a pivotal role in it.

Start typing and press Enter to search

Shopping Cart

No products in the cart.