Data Mining Definition :
Now a days one everyone must be aware that data mining is the most innovative as well as most used concept related to the database management techniques.Everyone has a question in mind about the Data Mining Definition and what are different Data Mining Examples.Everyone must be aware of data mining these days is an innovation also known as knowledge discovery process used for analyzing the different perspectives of data and encapsulate into proficient information.Before Data Mining Definition we must know the definition of Mining.The basic definition of mining is the process or industry of obtaining coal or other minerals from a mine. Just like that consider the Huge data and we need some specific information by analyzing that data.I have already gives some articles on Business Intelligence also. Before Business Intelligence the term data mining comes in to the picture.
Data Mining Definition (Different types)
Type 1 :
Data Mining is the process used for the extraction of hidden predictive data from huge databases.
Type 2 :
Data Mining is process of discovering the patterns in very large data sets involving the different methods like Machine Learning,statistics,different database systems.
Data mining is defined as a process used to extract usable data from a larger set of any raw data which implies analyzing data patterns in large batches of data using one or more software.
Type 4 :
The automated extraction of hidden data from a large amount of database is Data Mining.
Type 5 :
Data mining refers to the process of extracting the valid and previously unknown information from a large database to make crucial business decisions.
The process of data mining uses various tools which are used to predict the behavior of huge data which is further used to take decision. In this article i will focus on Data Mining Definition and basic concept of data mining.
Features of Data Mining :
• Automatic pattern predictions based on trend and behaviour analysis.
• Prediction based on likely outcomes.
• Creation of decision-oriented information.
• Focus on large data sets and databases for analysis.
• Clustering based on finding and visually documented groups of facts not previously known.
Data Mining Techniques :
Data mining involves effective data collection and warehousing as well as computer processing. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms. Data mining is also known as Knowledge Discovery in Data (KDD). There are so many Techniques of Data Mining. I will try to explain each and every technique individually in next articles.
- Decision Trees: It’s the most common technique used for data mining because of its simplest structure. The root of decision tree act as a condition or question with multiple answers. Each answer leads to specific data that help us to determine final decision based upon it.
- Sequential Patterns: The pattern analysis used to discover regular events, similar patterns in transaction data. Like, in sales; the historical data of customers helps us to identify the past transactions in a year. Based on the historic purchasing frequency of customer, the best deals or offers have been introduced by business firms.
- Clustering: Using the automatic method, cluster of objects is formed having similar characteristics. By using clustering, classes are defined and then suitable objects are placed in each class.
- Prediction: This method discovers the relationship between independent and dependent instances. For example, in the area of sales; to predict the future profit, sale acts as independent instance and profit could be dependent. Then based on historical data of sales and profit, associated profit is predicted.
- Association: Also called relation technique, in this a pattern is recognized based upon the relationship of items in a single transaction. It is suggested technique for market basket analysis to explore the products that customer frequently demands.
- Classification: Based upon machine learning, used to classify each item in a particular set into predefined groups. This method adopts mathematical techniques such as neural networks, linear programming, and decision trees and so on.
Data Mining Examples :
The above are some data mining techniques. As this article only focuses on the Data Mining Definition as well as example i will try to explain some examples which will gives you idea about data mining. As the importance of data analytics continues to grow, companies are finding more and more applications for Data Mining and Business Intelligence. This section gives you different Data Mining examples in real life.
1.Retail Sector :
Retail sector is one of the fastest growing sector in day to day life. There is huge data in retail and user needs to manage huge data with using different patterns.A customer who spends little but often and last did so recently will be handled differently to a customer who spent big but only once, and also some time ago. The former may receive a loyalty, up-sell and cross-sell offers, whereas the latter may be offered a win-back deal, for instance.
Amazon Used Data Mining Algorithms to fetch Today’s Deals
All of us know that E-commerce sector collects lot of historical data as well as current data. The data mining applications are checking that data with its predictive analysis algorithms and gives best seller options to customer. From various ways the E-commerce websites will use the data mining tools.Many E-commerce companies use Data Mining and Business Intelligence to offer cross-sells and up-sells through their websites.
One of the most famous of these is, of course, Amazon, who use sophisticated mining techniques to drive their, ‘People who viewed that product, also liked this’ functionality.
3.Mobile Service Providers :
The Mobile service providers uses huge data mining to collect customer data.Mobile phone and utilities companies use Data Mining and Business Intelligence to predict ‘churn’, the terms they use for when a customer leaves their company to get their phone/gas/broadband from another provider. They collate billing information, customer services interactions, website visits and other metrics to give each customer a probability score, then target offers and incentives to customers whom they perceive to be at a higher risk of churning.
4.Analytics Websites like Trivago :
There are different analytics websites which will compare the prices of different things from other website. The Analytics and data mining plays big role in that websites. If you check the website named Trivago which will gives the information of different hotel prices by comparing the different websites uses the predictive data mining technique which will mine the data from different websites and shows the results.
These are some most important examples of data mining. As i told you before that this article will focus on the Data Mining Definition as well as different data mining examples.Hope this article will helpful to you to understand the Basic Data Mining Definition and examples and to understand the data mining concept.