Web scraping vs. data mining: Understanding the differences
Thanks to a few indistinguishable similarities, you’ll find misconceptions about a website scraping tool working on similar lines as data mining.
As technology changes, it influences the companies relying on it and brings about tremendous functional changes. This trend witnessed exponential growth over the past decades and is predicted only to keep growing. With the rise of artificial intelligence and companies resorting to online business, there is a great need for data.
Working with a large amount of data is something that cannot be taken lightly. With this in mind, the website scraping tool came into being to gather data, help you understand customers, and serve different business purposes.
However, data and its associated phrases are thrown all around us confusingly and lead to misinterpretation. In this regard, here’s throwing some light on the differences between web scraping and data mining techniques.
What is Web Scraping?
Also termed as data extraction, web scraping works to extract data from poorly structured or unstructured data sources to a centralized location for further processing. These unstructured data sources may include emails, web pages, PDFs, documents, mainframe reports, scanned text, classifieds, spool files, etc. Cloud-based, on-site, or a hybrid of the two techniques serve for centralized storage purposes.
While it is possible to do web scraping manually, choose web scraping software tools to improve the speed and convenience. Typically, web scraping also formats the gathered data into a more convenient format, such as an excel sheet. But, remember, it only extracts data and does not include the analysis or processing that follows later.
Unlike other data extraction techniques, gathering data with a website scraping tool is quite simple. You don’t need to work with complicated algorithms, but only a scraper to fetch you the desired information.
What Goes on in Web Scraping?
The data extraction process with the scraping technique can be summarized in three simple steps:
The first step of any web scraping program is requesting a website for information from a specific URL. The response comes in HTML format and displays all the textual information of the specified web page.
Parse and Extract
Now, HTML has a simple structure and is a computer language. A parser works with any computer language and transforms text into a useful in-memory format for the computer to understand and workaround. With HTML parsing, the HTML code is extracted for meaningful information like headings, links, bold text, and text paragraphs.
The data is ultimately downloaded and saved in a JSON, CSV, or a database for further application or retrieval.
When Should You Use Web Scraping?
Web scraping is now widely used in many industries for meeting varying demands like those mentioned here:
- Content and News Collection: it is possible to obtain regular data feeds from multiple sources using content aggregation websites. This way, you can keep your site up-to-date and fresh.
- Lead Generation: Web scraping helps you extract data from multiple directories for generating business leads.
- Sentiment Analysis: data extraction from online sources helps you analyze the underlying attitudes of a product, brand, or phenomenon.
What is Data Mining?
Contrary to the popular notion, data mining is not just the process of acquiring data. The mining process begins after data gathering, when information is classified and analyzed for pattern recognition. Also termed as KDD or Knowledge Discovery from Data, the process uses complex algorithms, mathematical and statistical models to uncover trends and derive value from them.
What Happens in Data Mining?
There are seven steps to a data mining process:
Data Cleaning: Real-world data is often incomplete, noisy, and prone to errors. Hence, the first step is to clean this data for accurate results. Methods like filling the missing values, manual and automatic inspection are used here.
Integrate the Data: In this step, data from various sources like text files, databases, spreadsheets, data cubes, internet, etc. are extracted and integrated.
Data Selection: Not all integrated data may be needed for data mining. Hence, this step picks only useful information from the database.
Transformation of Data: Here, methods like normalization and aggregation transform the selected data into forms suitable for mining.
Mining: It includes intelligent processes like classification, regression, clustering, etc. to find data patterns.
Pattern Evaluation: This step identifies patterns that validate hypotheses, and those that are easy to understand and useful.
Knowledge Presentation: The mined data is finally presented with a knowledge presentation using visualization techniques.
When Should You Use Data Mining?
- Customer Segmentation: data mining helps businesses identify target customers’ characteristics and classify them for giving special offers that meet their needs.
- Detect Fraud: mining helps collect non-fraudulent and fraudulent reports and enables businesses to identify suspicious transactions.
- Discover Manufacture Patterns: manufacturers can use data mining to design systems based on relationships between customer needs, product architecture, and portfolio. It also predicts future product development time and costs.
Thanks to a few indistinguishable similarities, you’ll find misconceptions about a website scraping tool working on similar lines as data mining. However, they are intrinsically contrasting and are often used together by companies banking on business improvement data.
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