What is Data Mining and Data Extraction – A Full Overview
Data mining and data extraction (also known as web scraping) give similar meanings, leaving many people confused. Most of the time, the definition of data mining is often misinterpreted for scraping and obtaining data. But data mining is a more complicated process than that. This blog post will give you a clear picture of both data extraction and data mining.
What is Data Mining?
Data mining is also termed Knowledge Discovery in Database (KDD). It is a process that’s used to analyze large data sets using machine learning, statistical and mathematical techniques.
Data mining means to find and understand new and unseen knowledge in the data and ultimately understand patterns, trends, and relationships, and settle a value from it.
What is Data Extraction?
Data extraction goes by many different names, such as data scraping, data gathering, web scraping, data collection, parsing of data, etc. The technique is used to extract data (sometimes unstructured or poorly structured) from online sources into centralized storage locations for further processing.
Unstructured data include data from websites, documents, spooled files, emails, and so on. The centralized storage locations can be on-site, cloud-based, or a hybrid of both. Keep in mind that the process of extracting data doesn’t include processing or analysis. Those are done later after data storage and can be used for business intelligence purposes and other uses of analyzed data.
When compared with data mining, the wide use of the term data extraction is relatively low.
What can Data Mining and Data Extraction do?
With the automated mining process, data mining tools can move through databases to efficiently identify hidden patterns. Data mining can be used in business perspectives to look for data patterns and relationships to make better business decisions.
Data extraction goals can be categorized into three parts, and includes a process used for creating data warehouses, also known as ETL – Extract, Transform, Load.
• Archival – Converting physical formats such as newspapers and books into a digital format to backing up.
• Transferring the data format – It is possible to transfer data from one digital format to another. For example, you can move data from your current website to another website and collect that data with data extraction.
(this is where the ETL process comes into play)
• Data analysis – The common goal of data collection is to generate insights after analyzing the collected data.
*Note: Data analysis is not a part of the data extraction process, but it is the primary goal.
What is the Data Mining Process?
The data mining process can be categorized into seven steps;
• Data Cleaning – The data-world is not clean and well-structured all the time. It can be incomplete, noisy, and may contain errors as well. Hence, it’s essential to make sure that the data is clean and accurate. Automatic and manual inspection, filling in the missing values are some of the cleaning techniques.
• Data Integration – This step includes extracting, combining, and integrating data from various sources.
• Data Selection – As all the data is not practical, data that is useful will be retrieved from the database.
• Data Transformation – The selected data will be transformed into different forms for mining. This includes normalization, aggregation, generalization, etc.
• Data Mining – Intelligent methods are used to find patterns of data. This includes classification, regression, clustering, prediction, and many more.
• Anomaly Detection – Used to identify data that doesn’t match the expected pattern and detect its real cause.
• Pattern Evaluation – Involves identifying patterns that can be easily understood and be useful.
• Knowledge Representation – The mined data is represented using data visualization techniques.
What is the Data Extraction Process?
Data extraction can be considered as a part of the long process of data mining as well. The steps followed for this process are;
• Target source selection – Select the target source you intend to extract data from, such as a website.
• Data collection – This step involves sending a GET request to the website. Then parse the HTML document using programming languages such as Python, Ruby, PHP, etc.
• Data storage – The extracted data is stored in an on-site or cloud-based location.
The Differences Between Data Mining and Data Extraction
Some key differences in data mining and data extraction are pointed as follows;
• Data mining is also known as KDD (Knowledge Discovery in Databases), data/pattern analysis, knowledge extraction, and information harvesting.
Data extraction is similarly used with web scraping, web data extraction, data harvesting, web crawling, etc.
• The goal of the process of data mining is to make useful data available to generate more insights.
Data extraction involves collecting data so that they can be stored for later processing or analysis.
• Data mining usually studies structured data.
Data extraction deals mainly with unstructured or poorly structured data resources.
• Data mining‘s goal is to find information that is previously ignored or unknown.
Data extraction deals with existing data.
• The process of data mining can be complicated and may require staff training as well.
The data extraction process can be done efficiently and cost-effectively using the right tools and techniques.
How Can Residential Proxies Help with Data Mining and Data Extraction
Some of the benefits when using residential proxies for data mining and data extraction.
They are pointed out as follows;
Hide Your IP Address
If your location gets detected by some websites, your existing server will end up getting blocked, which you can solve by using a rotating residential proxy network. Rotating proxies will replace your IP address; hence, you will remain invisible and considered a “common” visitor to the target websites.
Additionally, you can connect with other proxy servers to access any website, no matter the servers’ locations.
Whatever the software tool used for data mining and data extraction, every tool takes some time to complete the data extraction process. Imagine yourself in a situation when the completion process is close, and suddenly, the connection fails. All your valuable time will be a waste due to an unreliable service that you use.
Therefore, regardless of the techniques you use, make sure to use a fast proxy provider to offer a fast and stable connection.
Whether the amounts of data you extract or mine, staying protected is a significant concern. There’s always a risk of exposing yourself and the data you gather with today’s cyber activities. Therefore, it is essential to get a server that provides extra security with every activity you perform involving data.