Data mining techniques consist of procedures to find hidden information, patterns, or for discovering knowledge from raw data, in this, you have to analyze millions of data and such type of work is done by Data mining engineer or data mining specialist they follow various types of data mining algorithms and data mining methods on big data, small data, Artificial intelligence which have a vast impact on marketing, they also detect fake data.
Fake data, fake news are mostly generated by bots, which are often doubled by massive bot networks, for corrupting correct information.
Data mining definition
Information economy when you do any task any transaction, social media post all includes data processing and data analysis. Data mining techniques include concepts where you all use the mathematical technique to use in the business world, health care processes for extracting information from various data resources.
Concept of “V” in Data mining
Previously you had a concept of three “v” in data mining.
Volume term related to the size of data named ‘Big Data’.
So if you consider an example of a Twitter server, a large amount of data is coming every day, so here data mining plays an important role, you have to use various types of data mining techniques for filtering fake data. So if companies use the wrong fake data then the discoverable data would be wrong.
Here velocity is related to the high speed of data.
Taking the same example of Twitter so around 6,000 tweets are sent every second, so you have massive data that should be processed so here data mining tools, data mining software plays an important role in data mining.
Here variety refers to different types of data like structured, semi-structured, and unstructured data.
- Structured Data- Such type of data that follows a particular order, easy to search usually.
Text data comes under Structured Data.
Examples: dates, phone number, pin code, address, name, etc., comes under structured Data.
- Semi-Structured Data-In this category combination of Structured and unstructured data comes.
Data that does not follow the SQL data structure comes under this.
Example: XML, JSON documents, etc.
- Unstructured data: In this category data are completely against SQL data format.
Data comes from rich media, IoT, AI these all come under unstructured data.
Example: image, audio, sensor data, ticket data, etc.
As a large amount of heterogeneous data are coming every day so Twitter data comes under semi-structured, as it is a combination of both structured and unstructured data.
Later on, IBM added a fourth “V” which stands for veracity.
It means related to truth means finding correct data or detecting fake news from the large pool of data. Fake news is mostly generated by bots to manipulate public opinion, so this concept helps to detect fake news.
After this one more “V” came which stands for value.
In this when raw data is converted into some informative data and can be used for the company. In this data with no value such as incomplete data are filtered out by using data mining methods.
Data mining techniques
Taking an example of image extraction of a cat, so with the use of AI technology bot, you can differentiate between cats and dog image and can get filtered data of cat image.
A few types of data mining techniques-
- Association Rule Mining
- Sequential pattern Mining
- Classification Analysis
- Clustering Analysis
Association Rule Mining
It is a technique in which you need to find an association and relationship, between different types of data set, for discovering our knowledge. For this, you have different types of Data mining software, using such types of software and data mining algorithm you can get clear filtered data set.
Sequential Pattern Mining
It is another well-known data mining technique in which you follow different types of sequence for extracting discovered data.
Sequential pattern mining technique is also used for Scientific Research.
In this technique, you just label out the data set with some known facts with some unknown facts, so that you can discover unknown facts from the raw data set with data mining concepts and techniques.
Again taking the example of COVID vaccine Research so while doing research we have some known facts about vaccines and some unknown facts about vaccines and implementing data mining techniques we discover some unknown facts about vaccines.
In this technique, you take the raw data sets and analyze them by data mining algorithm and cluster them in a group to discover knowledge from the clustered data set.
The clustering Analysis method is used for marketing, image processing, pattern recognition, etc. for improving data mining business intelligence.
For Clustering Analysis Machine learning.
Few Data mining benefits
With the help of data mining, you can prepare a database of filtered data and discovered data, as well as process and handle data in the least amount of time, thereby reducing costs.
Future trend prediction for the industry
With the help of data mining, we discover a clear dataset, and after analyzing with various tools and algorithms – future trends for the industry can be predicted.
Better decision making
You can take better decisions if you have mined a lot of past data for some purpose. For example, if you are taking data for weather reports, we can analyze them to predict upcoming weather patterns and natural calamities, thereby saving lives and improving the economy.
Data mining help in finding loopholes in security by analyzing huge chunks of data and give discovered datasets which helps in improving Security for an organization.
Data mining generally minimizes the data from a large bunch of raw data to discoverable data which is further used in various types of research, decision making, and also speeds up the processing time for data. It also gives future predictions for various research.