What is data mining?

Data mining is the process of extracting the useful information from huge data set or from large database.In other words,data mining is the abstracting of important data from huge database.Data mining can be implemented to relational databases,data warehouses, object-oriented databases, structured-unstructured databases, etc.

Some examples of data mining in day-to-day life:-

  1. Recommendations:-

After buying a product from online shopping websites like Amazon, Flipkart you will get a list of recommended products, and you end up buying one of those. By analyzing your past data this is possible to recommend and analyzing part will done by AI and ML.

2) Crime Prevention Agency:-

Crime prevention agencies also need data mining to studying and understanding patterns of crime,so that they can avoid crimes in future. This data includes detail information about crimes activities which are already happened.

Data Mining techniques:-

  1. Classification:-

Classification technique of data mining is used to classify data in different classes.It is based on machine learning.Classification is used to classify each item in a set of predefined data set of classes or groups.This technique use mathematical concept such as decision trees, linear programming, neural network, and statistics. In classification, there is a software that can learn how to classify the data items into groups.Algorithm used for classification is Logistic Regression,Naive Bayes,K-Nearest Neighbor.

2) Clustering:-

Clustering technique of data mining used to identify data that are like each other.This technique helps to understand similarities and differences between the data.This technique makes meaningful classes and objects and puts object in each class which having similar characteristics.Algorithm used for clustering is Hierarchical Clustering Algorithm,etc.

3) Regression:-

Regression is a technique of data mining which analyze the the relationship between variables.It creates predictive models.Regression technique can analyze and predict the results based on previously known data by applying formulas.Regression is very useful for finding the information on the basis of existing known information.Algorithms used for regression are Multivariate,Multiple Regression Algorithm,etc.

4) Association:-

Association is a technique that helps find the connection between two or more items.This technique can create a hidden pattern in data set.These pattern is discovered on the basis of relationship between items which are in same transaction.This technique is used in market basket analysis to identify a products that customers frequently purchase together.

5) Outer detection:-

Outer detection is a type of data mining technique that refers to observation of data items in the data-set which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc.

6) Sequential Patterns:-

Sequential pattern is a data mining technique that helps to create or find similar trends in transaction data for certain time of period.It is one of data mining technique that explore to discover or find similar patterns, trends in transaction data over a business period.With historical transaction data, vendor can identify items that customers buy together different times in a year. This technique will also help to customers to buy the product with better deals based on their previous purchased data.

7) Prediction:-

Prediction is a data mining technique which is a combination of other data mining techniques like sequential patterns,clustering,classification,etc.It analyzes past events for predicting a future event.The prediction technique can be used in the sale to predict profit/loss for the future.

Some best tools for data mining:-



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