Data mining is the process of extracting valuable information from large data sets. In the text analytics industry, data mining is used to discover relationships and patterns in text data that can be used to make predictions or recommendations. Outside of the text analytics industry, Data mining may refer to a variety of different activities, such as web mining, social media mining, or market research.
Steps for Data Mining
Data mining is a process that can be broken down into a few simple steps:
1. Collecting data: This step involves collecting data from a variety of sources, such as text documents, social media posts, or transaction records.
2. Preprocessing data: This step involves cleaning and organizing the data so that it can be analyzed.
3. Extracting features: This step involves extracting important information from the data that can be used to build models or make predictions.
4. Building models: This step involves building statistical models or machine learning algorithms that learn from the data and make predictions or recommendations.
5. Evaluating results: This final step involves evaluating the accuracy of the models and making decisions based on the results
When is Data Mining Done ?
Data mining is usually done when there is a large data set that needs to be analyzed for trends or patterns. It can be used to make predictions about future events or behavior.
Common Data Mining Techniques?
There are a variety of data mining techniques that can be used, depending on the type of data being mined and the goal of the mining. Some common techniques include:
- Clustering: This technique groups together data points that are similar to each other.
- Classification: This technique assigns data points to specific categories or classes.
- Regression: This technique finds relationships between different variables in the data set.
- Association Rules: This technique finds rules or associations between items in the data set.
Tools for Data Mining
There are a variety of software tools that can be used for data mining. Some common tools include:
SAS Enterprise Miner: This software from SAS Institute is used for a variety of data mining tasks, including clustering, classification, and regression.
IBM SPSS Modeler: This software from IBM is used for data mining and predictive analytics.
Oracle Data Mining: This software from Oracle Corporation is used for data mining and machine learning tasks.
Microsoft SQL Server Analysis Services: This software from Microsoft is used for data analysis and business intelligence tasks.
Data miners may also use a variety of open-source tools, such as R or Python, for data mining.