Data Mining Vs Web Scraping
Web Scraping vs Data Mining: What’s the Difference?
Web Scraping and Data Mining are two terms that are often used these terms do share many similarities, they are intrinsically, we’ll define each term and break down the differences between is Web Scraping? Web scraping refers to the extraction of data from any nerally, this also involves formatting this data into a more convenient format, such as an Excel sheetWhile web scraping can be done manually, in most cases web scraping software tools are preferred due to their speed and to learn more about web scraping? Check out our in-depth guide on web scraping and what it is used is Data Mining? Data Mining refers to the process of advance analysis of extensive data analyses can be advanced enough to require machine learning technologies in order to uncover specific trends or insights from the example, data mining might be used to analyze millions of transactions from a retailer such as Amazon to identify specific areas of growth and some cases, web scraping might be used to extract and build the data sets that will be used for further analysis via Data Scraping vs Data Mining: What’s the difference? At this point, the difference between these two terms should be pretty clear. But let’s put it into simpler scraping refers to the process of extracting data from web sources and structuring it into a more convenient format. It does not involve any data processing or mining refers to the process of analyzing large datasets to uncover trends and valuable insights. It does not involve any data gathering or mining does not involve data extraction. In fact, web scraping could be used in order to create the datasets to be used in Data osing ThoughtsThe confusion between these terms most likely stems from the similarities between Data Mining and Data Extraction (which shares more similarities with Web Scraping) you want to learn more about Data Extraction, check out our in-depth guide on data wnload ParseHub for free
What Is Web Scraping And How Does It Work? | Zyte.com
In today’s competitive world everybody is looking for ways to innovate and make use of new technologies. Web scraping (also called web data extraction or data scraping) provides a solution for those who want to get access to structured web data in an automated fashion. Web scraping is useful if the public website you want to get data from doesn’t have an API, or it does but provides only limited access to the data.
In this article, we are going to shed some light on web scraping, here’s what you will learn:
What is web scraping? The basics of web scrapingWhat is the web scraping process? What is web scraping used for? The best resources to learn more about web scraping
What is web scraping?
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.
If you’ve ever copy and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.
Web scraping is popular
And it should not be surprising because web scraping provides something really valuable that nothing else can: it gives you structured web data from any public website.
More than a modern convenience, the true power of data web scraping lies in its ability to build and power some of the world’s most revolutionary business applications. ‘Transformative’ doesn’t even begin to describe the way some companies use web scraped data to enhance their operations, informing executive decisions all the way down to individual customer service experiences.
The basics of web scraping
It’s extremely simple, in truth, and works by way of two parts: a web crawler and a web scraper. The web crawler is the horse, and the scraper is the chariot. The crawler leads the scraper, as if by hand, through the internet, where it extracts the data requested. Learn the difference between web crawling & web scraping and how they work.
The crawler
A web crawler, which we generally call a “spider, ” is an artificial intelligence that browses the internet to index and search for content by following links and exploring, like a person with too much time on their hands. In many projects, you first “crawl” the web or one specific website to discover URLs which then you pass on to your scraper.
The scraper
A web scraper is a specialized tool designed to accurately and quickly extract data from a web page. Web scrapers vary widely in design and complexity, depending on the project. An important part of every scraper is the data locators (or selectors) that are used to find the data that you want to extract from the HTML file – usually, XPath, CSS selectors, regex, or a combination of them is applied.
The web data scraping process
If you do it yourself
This is what a general DIY web scraping process looks like:
Identify the target websiteCollect URLs of the pages where you want to extract data fromMake a request to these URLs to get the HTML of the pageUse locators to find the data in the HTMLSave the data in a JSON or CSV file or some other structured format
Simple enough, right? It is! If you just have a small project. But unfortunately, there are quite a few challenges you need to tackle if you need data at scale. For example, maintaining the scraper if the website layout changes, managing proxies, executing javascript, or working around antibots. These are all deeply technical problems that can eat up a lot of resources. There are multiple open-source web data scraping tools that you can use but they all have their limitations. That’s part of the reason many businesses choose to outsource their web data projects.
If you outsource it
1. Our team gathers your requirements regarding your project.
2. Our veteran team of web data scraping experts writes the scraper(s) and sets up the infrastructure to collect your data and structure it based on your requirements.
3. Finally, we deliver the data in your desired format and desired frequency.
Ultimately, the flexibility and scalability of web scraping ensure your project parameters, no matter how specific, can be met with ease. Fashion retailers inform their designers with upcoming trends based on web scraped insights, investors time their stock positions, and marketing teams overwhelm the competition with deep insights, all thanks to the burgeoning adoption of web scraping as an intrinsic part of everyday business.
What is web scraping used for?
Price intelligence
In our experience, price intelligence is the biggest use case for web scraping. Extracting product and pricing information from e-commerce websites, then turning it into intelligence is an important part of modern e-commerce companies that want to make better pricing/marketing decisions based on data.
How web pricing data and price intelligence can be useful:
Dynamic pricingRevenue optimizationCompetitor monitoringProduct trend monitoringBrand and MAP compliance
Market research
Market research is critical – and should be driven by the most accurate information available. High quality, high volume, and highly insightful web scraped data of every shape and size is fueling market analysis and business intelligence across the globe.
Market trend analysisMarket pricingOptimizing point of entryResearch & developmentCompetitor monitoring
Alternative data for finance
Unearth alpha and radically create value with web data tailored specifically for investors. The decision-making process has never been as informed, nor data as insightful – and the world’s leading firms are increasingly consuming web scraped data, given its incredible strategic value.
Extracting Insights from SEC FilingsEstimating Company FundamentalsPublic Sentiment IntegrationsNews Monitoring
Real estate
The digital transformation of real estate in the past twenty years threatens to disrupt traditional firms and create powerful new players in the industry. By incorporating web scraped product data into everyday business, agents and brokerages can protect against top-down online competition and make informed decisions within the market.
Appraising Property ValueMonitoring Vacancy RatesEstimating Rental YieldsUnderstanding Market Direction
News & content monitoring
Modern media can create outstanding value or an existential threat to your business – in a single news cycle. If you’re a company that depends on timely news analyses, or a company that frequently appears in the news, web scraping news data is the ultimate solution for monitoring, aggregating, and parsing the most critical stories from your industry.
Investment Decision MakingOnline Public Sentiment AnalysisCompetitor MonitoringPolitical CampaignsSentiment Analysis
Lead generation
Lead generation is a crucial marketing/sales activity for all businesses. In the 2020 Hubspot report, 61% of inbound marketers said generating traffic and leads was their number 1 challenge. Fortunately, web data extraction can be used to get access to structured lead lists from the web.
Brand monitoring
In today’s highly competitive market, it’s a top priority to protect your online reputation. Whether you sell your products online and have a strict pricing policy that you need to enforce or just want to know how people perceive your products online, brand monitoring with web scraping can give you this kind of information.
Business automation
In some situations, it can be cumbersome to get access to your data. Maybe you need to extract data from a website that is your own or your partner’s in a structured way. But there’s no easy internal way to do it and it makes sense to create a scraper and simply grab that data. As opposed to trying to work your way through complicated internal systems.
MAP monitoring
Minimum advertised price (MAP) monitoring is the standard practice to make sure a brand’s online prices are aligned with their pricing policy. With tons of resellers and distributors, it’s impossible to monitor the prices manually. That’s why web scraping comes in handy because you can keep an eye on your products’ prices without lifting a finger.
Learn more about web scraping
Here at Zyte (formerly Scrapinghub), we have been in the web scraping industry for 12 years. With our data extraction services and automatic web scraper, Zyte Automatic Extraction, we have helped extract web data for more than 1, 000 clients ranging from Government agencies and Fortune 100 companies to early-stage startups and individuals. During this time we gained a tremendous amount of experience and expertise in web data extraction.
Here are some of our best resources if you want to deepen your web scraping knowledge:
What are the elements of a web scraping project? Web scraping toolsHow to architect a web scraping solutionIs web scraping legal? Web scraping best practices
Data Mining VS Data Extraction: What’s the Difference?
As two typical buzzwords related to data science, data mining and data extraction confuse a lot of people. Data mining is often misunderstood as extracting and obtaining data, but it is actually way more complicated than that. In this post, let’s find out the difference between data mining and data extraction.
Table of contents
What is Data Mining?
What Can Data Mining Do?
Some Use Cases of Data Mining
The Overall Steps of Data Mining
Disadvantages of Data Mining
What is Data Extraction?
What Can Data Extraction Do?
Some Use Cases of Data Extraction
The Overall Steps of Data Extraction
Disadvantages of Data Extraction
Key Differences Between Data Mining and Data Extraction
Conclusion – Data Mining Vs Data Extraction
Data mining, also referred to as Knowledge Discovery in Database (KDD), is a technique often used to analyze large data sets with statistical and mathematical methods to find hidden patterns or trends, and derive value from them.
By automating the mining process, data mining tools can sweep through the databases and identify hidden patterns efficiently. For businesses, data mining is often used to discover patterns and relationships in data to help make optimal business decisions.
After data mining became widespread in the 1990s, companies in a wide array of industries – including retail, finance, healthcare, transportation, telecommunication, E-commerce, etc started to use data mining techniques to generate insights from data. Data mining can help segment customers, detect fraud, forecast sales and many more. Specific uses of data mining include:
Customer segmentation
Through mining customer data and identifying the characteristics of target customers, companies can align them into a distinct group and provide special offers that cater to their needs.
Market basket analysis
This is a technique based on a theory that if you buy a certain group of products, you are likely to buy another group of products. One famous example is that when fathers buy diapers for their infants, they tend to buy beers together with the diapers.
Forecasting sales
It may sound similar to market basket analysis, but this time data mining is used for predicting when a customer will buy a product again in the future. For instance, a coach buys a bucket of protein powder that should last 9 months. The store that sold the protein powder would plan to release new protein powder 9 months later so that the coach would buy it again.
Detecting frauds
Data mining aids in building models to detect fraud. By collecting samples of fraudulent and non-fraudulent reports, businesses are empowered to identify which transactions are suspicious.
Discover patterns in manufacturing
In the manufacturing industry, data mining is used to help design systems by uncovering the relationships between product architecture, portfolio, and customer needs. It can also predict future product development time span and costs.
Above are just a few scenarios that data mining is used. For more use cases, check out Data Mining Applications and Use Cases.
Data mining is an intact process of gathering, selecting, cleaning, transforming, and mining the data, in order to evaluate patterns and deliver value in the end.
(Source)
Generally, the data mining process can be summarized into 7 steps:
Step 1: Data Cleaning
In the real world, data is not always cleaned and structured. It is often noisy, incomplete and may contain errors. To make sure the data mining result is accurate, data needs to be cleaned first. Some cleaning techniques include filling in the missing values, automatic and manual inspection, etc.
Step 2: Data Integration
This is the step where data from different sources is extracted, combined and integrated. These sources can be databases, text files, spreadsheets, documents, data cubes, the Internet and so on.
Step 3: Data Selection
Usually, not all data integrated is needed for data mining. Data selection is where only useful data is selected and retrieved from the large database.
Step 4: Data Transformation
After data is selected, it is transformed into suitable forms for mining. This process involves normalization, aggregation, generalization, etc.
Step 5: Data Mining
Here comes the most important part of data mining – using intelligent methods to find patterns in data. The data mining process includes regression, classification, prediction, clustering, association learning and many more.
Step 6: Pattern Evaluation
This step aims at identifying potentially useful and easy to understand patterns, as well as patterns that validate hypotheses.
Step 7: Knowledge Representation
In the final step, the information mined is presented with knowledge representation and visualization techniques in an appealing way.
Though data mining is useful, it has some limitations.
High investments in time and labor
Because it is a long and complicated process, it needs extensive work from high-performance and skilled staff. Data mining specialists can take advantage of powerful data mining tools, yet they require specialists to prepare the data and understand the output. As a result, it may still take some time to process all the information.
Privacy & data safety issues
As data mining gathers customers’ info with market-based techniques, it may violate the privacy of users. Also, hackers may hack the data stored in mining systems, which poses a threat to customer data security. If the data stolen is misused, it can easily harm others.
Above is a brief introduction to data mining. As I’ve mentioned, data mining contains the process of data gathering and data integration, which includes the process of data extraction. In this case, it is safe to say data extraction can be a part of the long process of data mining.
Also known as “web data extraction” and “web scraping”, data extraction is the act of retrieving data from (usually unstructured or poorly structured) data sources into centralized locations for storage or further processing.
Specifically, unstructured data sources include web pages, emails, documents, PDFs, scanned text, mainframe reports, spool files, classifieds, etc. The centralized locations may be on-site, cloud-based, or a hybrid of the two. It is important to keep in mind that data extraction doesn’t include the processing or analysis that may take place later.
In general, the goals of data extraction fall into 3 categories.
Archival
Data extraction can convert data from physical formats (such as books, newspapers, invoices) into digital formats (such as databases) for safekeeping or as a backup.
Transfer the format of data
If you want to transfer the data from your current website into a new website that is under development, you can collect data from your own website by extracting it.
Data analysis
As the most common goal, the extracted data can be further analyzed to generate insights. This may sound similar to the data analysis process in data mining, but note that data analysis is the goal of data extraction, not part of its process. What’s more, the data is analyzed differently. One example is that e-store owners extract product details from eCommerce websites like Amazon to monitor competitors’ strategies in real-time.
Just like data mining, data extraction is an automated process that comes with lots of benefits. In the past, people used to copy and paste data manually from one place to another to move data, which is extremely time-consuming. Data extraction speeds up the collecting, and largely increases the accuracy of data extracted. For other advantages of data extraction, you may view this article.
Similar to data mining, data extraction has been widely used in multiple industries serving different purposes. Besides monitoring prices in eCommerce, data extraction can help in individual paper research, news aggregation, marketing, real estate, travel and tourism, consulting, finance, and many more.
Lead generation
Companies can extract data from directories like Yelp, Crunchbase, Yellowpages and generate leads for business development. You can check out this video to see how to extract data from Yellowpages with a web scraping template.
Content & news aggregation
Content aggregation websites can get regular data feeds from multiple sources and keep their sites fresh and up-to-date.
Sentiment analysis
After extracting the online reviews/comments/feedback from social media websites like Instagram and Twitter, people can analyze the underlying attitudes and get an idea of how they are perceiving a brand, product or phenomenon.
Data extraction is the first step of ETL(extract, transform, and load) and ELT(extract, load, and transform). ETL and ELT are themselves part of a complete data integration strategy. In other words, data extraction can be part of data mining.
While data mining is all about gaining actionable insights from large data sets, data extraction is a much shorter and straight-forward process. The data extraction process can be summarized into three steps.
Step 1: Select a data source
Choose the target data source you want to extract, such as a website.
Step 2: Data Collection
Send a “GET” query to the website and parse the HTML document of it with programming languages like Python, PHP, R, Ruby, etc.
Step 3: Data Storage
Store the data in your on-site database or a cloud-based destination for future use.
If you are an experienced programmer who wants to extract data, the above steps may sound easy to you. However, if you are a non-coder, there is a shortcut – using data extraction tools like Octoparse. Data extraction tools, just like data mining tools, are developed to save people energy and make data processing simple to everyone. These tools are not only cost-effective but also beginner-friendly. They allow users to crawl the data within minutes, store it in the cloud and export it into many formats such as Excel, CSV, HTML, JSON or on-site databases via APIs.
Server breakdown
When extracting data at a large scale, the webserver of the target website may overload and this could lead to a server breakdown, which harms the interest of the site owner.
IP banning
When one is crawling data too frequently, websites can block his/her IP address. It may totally ban the IP or restrict the crawler’s access to breakdown the extraction. To extract data without getting blocked, people need to extract data at a moderate speed and adopt some anti-blocking methods.
Legal issues
Web data extraction is in a grey area when it comes to legality. Big sites like Linkedin and Facebook state clearly in their Terms of Service that any automated extraction of data is disallowed. There have been many lawsuits between companies over scraping bot activities.
Data mining is also named knowledge discovery in databases, knowledge extraction, data/pattern analysis, information harvesting. Data extraction is used interchangeably with web data extraction, web scraping, web crawling, data retrieval, data harvesting, etc.
Data mining studies are mostly on structured data, while data extraction usually retrieves data out of unstructured or poorly structured data sources.
The goal of data mining is to make available data more useful for generating insights. Data extraction is to collect data and gather them into a place where they can be stored or further processed.
Data mining is based on mathematical methods to reveal patterns or trends. Data extraction is based on programming languages or data extraction tools to crawl the data sources.
The purpose of data mining is to find facts that are previously unknown or ignored, while data extraction deals with existing information.
Data mining is much more complicated and requires large investments in staff training. Data extraction, when conducted with the right tool, can be extremely easy and cost-effective.
These terms have been around for about two decades. Data extraction can be part of data mining where the aim is collecting and integrating data from different sources. Data mining, as a relatively complex process, comes as discovering patterns for making sense of data and predicting the future. Both require different skill sets and expertise, yet the increasing popularity of non-coding data extraction tools and data mining tools greatly enhances productivity and makes people’s lives much easier.
Author: Milly
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Frequently Asked Questions about data mining vs web scraping
Is web scraping data extraction?
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
What is the difference between data mining and data extraction?
Data mining is based on mathematical methods to reveal patterns or trends. Data extraction is based on programming languages or data extraction tools to crawl the data sources. The purpose of data mining is to find facts that are previously unknown or ignored, while data extraction deals with existing information.Jan 20, 2021
Is it legal to scrape data from websites?
It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit. For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal.Aug 16, 2021
