Data mining is a technique to explore and analyse the data using the computational algorithms. The analysis of data results some similar or dissimilar patterns. That are used for designing and developing different applications such as recognition, decision making and others. Data mining supports various kinds of data modeling such as classification, prediction, association, cluster analysis and others. The mining and their techniques can be depends upon the application. Data mining algorithms consumes data samples, which is supplied for performing the mining.
In the real world, huge amount of data are available. This data is belongs from various domains such as education, medical and others. This data may be used for extracting knowledge and information for making decision and recognizing similar patterns. For example, we can find sales patterns in a month from some shopping database. Data can be analyzed, summarized, visualized to understand and meet to challenges . The goals of data mining are fast retrieval of data, knowledge Discovery, identification of hidden patterns, reduce level of complexity, etc . Data mining is treated as knowledge discovery in database (KDD process). KDD is an iterative process it includes the following steps.
figure 1 data mining process
Types of Data Mining System
Data mining systems can be categorized according to various criteria the classification is as follows :
- Classification of data mining systems according to the type of data source mined: In an organization a huge amount of data’s are available where we need to classify these data but these are available most of times in a similar fashion. We need to classify these data according to its type (maybe audio/video, text format etc).
- Classification of data mining systems according to the data model: There are so many number of data mining models (Relational data model, Object Model, Object Oriented data Model, Hierarchical data Model/W data model)are available and each and every model we are using the different data. According to these data model the data mining system classify the data in the model.
- Classification of data mining systems according to the kind of knowledge discovered: This classification based on the kind of knowledge discovered or data mining functionalities, such as characterization, discrimination, association, classification, clustering, etc. Some systems tend to be comprehensive systems offering several data mining functionalities together.
- Classification of data mining systems according to mining techniques used: This classification is according to the data analysis approach used such as machine learning, neural networks, genetic algorithms, statistics, visualization, database oriented or data warehouse-oriented, etc.
Applications of Data mining 
this section provides overview of Data mining applications.
- Financial services: Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.
- Health care: Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
- Marketing and sales: Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail
- Transportation: Analyzing data to identify patterns and trends is to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations
- Government: Government agencies such as public safety and utilities have a particular need for machine learning. They have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
 Er. Rimmy Chuchra, “Use of Data Mining Techniques for the Evaluation of Student Performance : A Case Study”, International Journal of Computer Science and Management Research, Volume 1, Issue 3, October 2012.
 Aakanksha Bhatnagar, Shweta P. Jadye, Madan Mohan Nagar, “Data Mining Techniques & Distinct Applications: A Literature Review”, International Journal of Engineering Research & Technology (IJERT), Vol. 1, Issue 9, November- 2012
 Dunham, M. H., Sridhar S., “Data Mining: Introductory and Advanced Topics”, Pearson Education, New Delhi, ISBN: 81-7758-785-4, 1st Edition, 2006
 Alex Smola and S. V. N. Vishwanathan, “Introduction to Machine Learning”, Yahoo! Labs, Ph. D thesis, Cambridge University Press. https://www.cse.iitb.ac.in/~ganesh/noml2015/bookchaptersvn.pdf