These two Data Science buzzwords confuse a lot of people. Data Mining is often misunderstood as extracting and retrieving data, but the reality is much more complex. In this post, let's dot Mining and find out the difference between Data Mining and Data Extraction.
What is Data Mining?
Data mining, also called Database Knowledge Discovery (KDD), is a technique often used to analyze large datasets using statistical and mathematical methods to find hidden patterns or trends and extract value from them.
What can be done with Data Mining?
By automating the process, can browse databases and effectively uncover hidden patterns. For businesses, data mining is often used to discover patterns and relationships in data to help make better business decisions.
Application examples
After data mining became widespread in the 1990s, companies in a wide range of industries, including retail, finance, healthcare, transportation, telecommunications, e-commerce, etc., began to use data mining methods to obtain information on data basis. Data mining can help segment customers, identify fraud, predict sales, and more.
- Customer segmentation
By analyzing customer data and identifying the traits of target customers, companies can group them into a separate group and provide special offers that meet their needs. - Market Basket Analysis
This technique is based on the theory that if you buy a certain group of products, you are more likely to buy a different group of products. One famous example: when fathers buy diapers for their babies, they tend to buy beer along with the diapers. - Sales forecasting
It may seem similar to market basket analysis, but this time data analysis is used to predict when a customer will buy a product again in the future. For example, a coach buys a can of protein that should last for 9 months. The store that sells this protein plans to release a new one in 9 months so that the coach will buy it again. - Fraud detection
Data mining helps in building models for fraud detection. By collecting samples of fraudulent and truthful reports, businesses are empowered to determine which transactions are suspicious. - Pattern detection in production
In the manufacturing industry, data mining is used to help design systems by identifying the relationship between product architecture, profile, and customer needs. Data mining can also predict product development times and costs.
And these are just a few use cases for data mining.
Stages of data mining
Data mining is a holistic process of collecting, selecting, cleaning, transforming, and extracting data in order to evaluate patterns and, ultimately, extract value.
Generally, the entire data mining process can be summarized into 7 steps:
- Data Cleaning
In the real world, data is not always cleaned and structured. They are often noisy, incomplete, and may contain errors. To make sure the data mining result is accurate, you first need to clean up the data. Some cleaning methods include filling in missing values, automatic and manual controls, and so on. - Data integration
This is the stage where data from different sources are extracted, combined and integrated. Sources can be databases, text files, spreadsheets, documents, multidimensional datasets, the Internet, and so on. - Data sampling
Usually, not all integrated data is needed in data mining. Data sampling is the stage in which only useful data is selected and extracted from a large database. - Data conversion
Once the data is selected, it is converted into forms suitable for mining. This process includes normalization, aggregation, generalization, etc. - Data mining
Here comes the most important part of data mining - using intelligent methods to find patterns in them. The process includes regression, classification, prediction, clustering, association learning, and more. - Model evaluation
This step aims to identify potentially useful, easy-to-understand patterns, as well as patterns that support hypotheses. - Knowledge representation
At the final stage, the information obtained is presented in an attractive way using knowledge representation and visualization methods.
Disadvantages of Data Mining
- Large investment of time and labor
Since data mining is a long and complex process, it requires a lot of work from productive and skilled people. Data scientists can use powerful data mining tools, but they need experts to prepare the data and understand the results. As a result, it may take some time to process all the information. - Data privacy and security
Since data mining collects information about customers through market methods, it can violate user privacy. In addition, hackers can obtain data stored in data mining systems. This poses a threat to the security of customer data. If the stolen data is misused, it can easily harm others.
The above is a brief introduction to data mining. As I already mentioned, data mining contains the process of collecting and integrating data, which includes the process of extracting data (data extraction). In this case, it's safe to say that data extraction can be part of a long data mining process.
What is Data Extraction?
Also known as "web data mining" and "web scraping", this process is the act of extracting data from (usually unstructured or poorly structured) data sources into centralized locations and centralization in one location for storage or further processing. Specifically, unstructured data sources include web pages, email, documents, PDF files, scanned text, mainframe reports, reel files, announcements, and so on. Centralized storage can be local, cloud or hybrid. It is important to remember that data extraction does not include processing or other analysis that may occur later.
What can be done with Data Extraction?
Basically, data extraction purposes fall into 3 categories.
- Archiving
Data extraction can convert data from physical formats such as books, newspapers, invoices to digital formats such as databases for storage or backup. - Changing the data format
When you want to migrate data from your current site to a new one under development, you can collect data from your own site by extracting it. - ΠΠ½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ
It is common to further analyze the extracted data to gain insight into it. This may sound similar to data mining, but keep in mind that data mining is the goal of data mining, not part of it. Moreover, the data is analyzed differently. One example is that online store owners pull product information from e-commerce sites like Amazon to monitor competitor strategies in real time. Like data mining, data extraction is an automated process with many benefits. In the past, people copied and pasted data manually from one place to another, which was very time consuming. Data extraction speeds up collection and greatly improves the accuracy of the extracted data.
Some examples of using Data Extraction
Similar to data mining, data mining is widely used in various industries. In addition to e-commerce price monitoring, data mining can help with your own research, news aggregation, marketing, real estate, travel and tourism, consulting, finance, and more.
- Lead generation
Companies can extract data from directories: Yelp, Crunchbase, Yellowpages and generate leads for business development. You can watch the video below to learn how to extract data from Yellowpages with . - Aggregation of content and news
Content aggregating websites can receive regular data feeds from multiple sources and keep their sites up to date. - Mood analysis
After extracting reviews, comments, and testimonials from social networks such as Instagram and Twitter, professionals can analyze the underlying attitudes and gain insights into how a brand, product, or phenomenon is perceived.
Data Extraction Steps
Data extraction is the first stage of ETL (Extract, Transform, Load: Extract, Transform, Load) and ELT (Extract, Load, and Transform). ETL and ELT are themselves part of a complete data integration strategy. In other words, extracting data can be part of their extraction.
Extract, transform, load
While data mining is all about extracting information from large amounts of data, data extraction is a much shorter and simpler process. It can be reduced to three stages:
- Selecting a data source
Select the source you want to extract data from, such as a website. - Π‘Π±ΠΎΡ Π΄Π°Π½Π½ΡΡ
Send a "GET" request to the site and parse the resulting HTML document using programming languages ββsuch as Python, PHP, R, Ruby, etc. - Data Storage
Save the data to your local database or cloud storage for future use. If you are an experienced programmer who wants to extract data, the above steps may seem simple to you. However, if you are not a programmer, there is a shortcut - use data mining tools like . Data extraction tools, just like data mining tools, are designed to save energy and make data processing easy for everyone. These tools are not only economical, but also beginner-friendly. They allow users to collect data within minutes, store it in the cloud, and export it to many formats: Excel, CSV, HTML, JSON, or to databases on the site via an API.
Disadvantages of Data Extraction
- Server crash
When extracting data on a large scale, the web server of the target site may be overloaded, which can lead to a server crash. This will harm the interests of the site owner. - Ban by IP
When a person collects data too often, websites can block their IP address. A resource can completely ban an IP address or restrict access by making the data incomplete. To retrieve data and avoid blocking, you need to do it at a moderate speed and apply some anti-blocking techniques. - Problems with law
Extracting data from the web falls into a gray area when it comes to legality. Major sites such as Linkedin and Facebook clearly state in their terms of use that any automatic extraction of data is prohibited. There have been many lawsuits between companies due to bot activities.
Key Differences Between Data Mining and Data Extraction
- Data mining is also called knowledge discovery in databases, knowledge extraction, data/pattern analysis, information gathering. Data extraction is used interchangeably with web data extraction, web page scanning, data collection, and so on.
- Data mining research is mostly based on structured data whereas data mining usually draws from unstructured or poorly structured sources.
- The goal of data mining is to make data more useful for analysis. Data extraction is the collection of data into one place where it can be stored or processed.
- Analysis in data mining is based on mathematical methods for identifying patterns or trends. Data extraction is based on programming languages ββor data extraction tools to bypass sources.
- The purpose of data mining is to find facts that were not previously known or ignored, while data extraction deals with existing information.
- Data mining is more complex and requires a large investment in training people. Data extraction with the right tool can be extremely easy and cost effective.
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