Data mining is the method of extracting patterns from data. Data mining is becoming more and more vital tool to transform this data into information. It is extensively used in profiling practices.
Data mining is a powerful technology that predicts future trends and behaviours. Data mining techniques can also be implemented on existing software and hardware to increase the value of existing software.
How does Data Mining Works?
Question is how data mining works and how it is beneficial for the business?
Data mining tell us that what is going to happen next? The techniques that are use for this purpose are called Modeling. Modeling is on the whole the act of making a model in one situation that u know and apply that model in the situation that you don’t know. Data mining software analyzes relationships and patterns. Many types of analytical software are available such as statistical, machine learning, and neural networks. By and large these four types of relationships which are required in data mining:
Classes: Stored data is used to trace data in prearranged groups.
Clusters: Data items are grouped on the basis of logical relationships or customer preferences.
Associations: This is also called Associative mining. Data can also be extracted to identify associations.
Sequential patterns: Data is extract to predict behaviour patterns and trends.
Stages of Data mining process:
Data gathering: First of all we have to collect and analyze the data.
Data cleaning: This is the second stage. Data cleaning is detecting and correcting the inaccurate data.
Feature extraction: In feature extraction we get only the important characteristic of the data.
Pattern extraction and discovery: This stage is often called as “Data mining”. And this is the most important stage of data mining where we have to put our all effort.
Visualization of data: Data visualization is to let the user understand what is going on. Data mining usually involves the extraction of hidden data from database that the user did not already know about.
Evaluation of result: This is the last stage of this process. In this stage we closely observe the given data and find out the correct result.
Data Mining Techniques
Basically we have two Data mining techniques
Predictive data mining: Predictive data mining uses historical data to predict about future theories. It merges database analysis with artificial intelligence.
Predictive data mining is further categorized into:
Descriptive data mining: Descriptive data mining is to find patterns in the data. They are generally used to create meaningful subgroups. Descriptive data mining is further classified into
- Sequential analysis.
Data Mining can be used in any organization that needs to find patterns or relationships in their data and it is increasing rapidly. It can be used in every kind of business.
Here are some applications of data mining:
- Direct Marketing
- Fraud Detection
- Customer Churn
- Sky Survey Cataloging
- Document Clustering
- Market Segmentation
- Electronic commerce
- New product development
- Government policy setting
- Medical management