Data mining is a fast-growing technology in the world. It is a major part of data science. That is why the popularity of data mining gained a boost in the past few years. Here in this blog post, I will share with you everything about data mining and the top techniques used in the world of data mining. Let’s explore it now:-
What is data mining?
Data is enormous, and it doesn’t stop there; it is growing at a pace beyond imagination! Every field, every sector, every industry has big data to deal with regularly. But, what’s the point of having such huge amounts of data unless it is possible to process it in such a manner that meaningful insights can be drawn later? It is exactly where data mining comes into play. Transforming raw data into a form that makes it possible to draw conclusions and aid in decision-making is what data mining is all about. Now, many might wonder how the extraction of useful data is done? Well, all of this is possible because of machine learning, statistics, Artificial intelligence, etc.
Data mining is nothing but looking for patterns and trends in the data using complex algorithms.
Having understood what data mining is, let’s delve deeper into this.
Why is data mining important?
Since data mining is all about transforming raw data into a form that results in the most useful information, companies/firms/organizations/industries can use this useful data to understand their customers better. Not just this. With the transformed data, they can engage in more effective marketing strategies that’d benefit their customers and reduce the costs for the companies. Ultimately, the companies will increase their sales, decrease expenses and improve their customer base.
What type of data can be mined?
Following data types can be mined to yield the best possible results.
- Relational databases: This consists of data items with predefined relationships between them.
- Data warehouse: It is a single storage place that collects and stores data from several sources.
- Transactional data: As evident as it can get, this consists of data in transactions like flight booking, purchases by the customers, etc.
- Advanced DB and information repositories: It is a large database infrastructure that consists of data sets isolated to be mined for data reporting and analysis.
- Object-oriented and object-relational databases: Constitutes complex data and also relationships between them.
- Multimedia and streaming database: Data about multimedia and streaming applications like data related to watching movies, videos, etc.
- Text databases: Huge data in the form of text.
- Text mining and Web mining: Text mining revolves around converting unstructured text into structured data, and web mining is all about converting raw unstructured web content into a structured format.
Data mining Techniques
Data mining employs certain analysis tools to develop meaningful data that makes the decision-making process easier than ever for companies. These analysis tools make use of statistical models, machine learning techniques, and mathematical algorithms, etc.
Recent times have seen a lot of data mining techniques being employed. Some of them are –
It is one of those techniques used to retrieve important and relevant information about data and metadata. As evident as its name, this technique is used to classify data. Data is associated with various attributes, and after the organizations identify these characteristics, they can proceed with the classification aspect. Mostly, classification is based on the type of data sources mined, the databases involved, mining techniques used, to name a few.
It is nothing but identifying objects that are like each other. Simply put, it is that data mining technique that aims at identifying similar data. With this technique, the similarities and differences between the data objects can be taken note of. Clustering usually makes use of graphics to see how data is distributed to identify trends.
Many people tend to get confused between clustering and classification. Classification puts objects into predefined classes, whereas in clustering, objects are put in classes defined by it. Also, clustering involves grouping chunks of data based on their similarities, contrary to what is seen in classification.
Regression is one of those statistical methods used to determine the relationship between one dependent variable and other independent variables. The dependent variable is represented by “Y”. With regression, one can identify and analyze the relationship between variables and how it is affected by certain other factors. The weight of the factors can also be manipulated to arrive at better conclusions. For example, how will the crop yield depend on factors like rainfall, fertilizer quality and quantity, and a lot more?
The association or link between two or more items is the key objective here. This technique finds a hidden pattern between the data sets. It makes use of if-then statements to find the link between the data items. It follows the statistical concept of correlation.
This technique, also known as outlier mining or outlier analysis, revolves around the observation of those data items in the dataset which do not match an expected pattern or expected behavior. That data item that diverges way too much from the rest is known as an outlier. Some of the key areas that use this technique are credit or debit card fraud detection, detecting outlying in wireless sensor network data, network interruption identification, intrusion, etc.
This technique mainly uses transactional data to identify similar trends, patterns, and events in it over a while. With this technique, one can discover similar patterns in transactional data over a certain period. This technique helps companies develop better recommendations for their customers and discounts and similar deals for the recommended items, thereby shooting up the sales.
As the name suggests, this technique is used to predict a future event by considering past events or instances.
Applications of data mining
Proper implementation of techniques yields great results. Because of this, data mining has immense applications in a lot of fields like healthcare, finance, education, agriculture, E-commerce, fraud detection, and preventing crimes, among others.
Let’s Sum Up
No wonder data is huge, and the requirement of techniques to make the best possible use of this data is critical. Understanding what data mining is, how data can be transformed so that it aids in better decision-making, the techniques to do so, etc., are critical.