What is Web Data Mining? How Does it Differ from Web Scraping? (1)

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Employ Remote

• What is Web Data Mining? How Does it Differ from
  Web Scraping?

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What is Web Scraping?

• Web scraping and data mining - these two terms
  sound like buzzwords. Data mining is often
  misunderstood to mean the process of extracting
  information from websites. This article will
  explain what data mining is, how it differs from
  web scraping, and why you should outsource web
  data mining services.

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What is Web Data Mining? How Does it Differ from
Web Scraping?

• Data mining is similar to mining for gold, where
  you dig through rocks to find wealth. It involves
  sorting through large data sets to obtain the
  information your business needs. It is an
  integral part of data science and analytics. Data
  mining can be confused with web scraping. Data
  mining does not involve data extraction,
  scraping, or data gathering.
• Data mining is the process of analyzing large
  quantities of data to provide useful insights and
  trends for businesses that rely on it. Web
  scraping, also known as web data extraction, is
  the automated collection of data from websites.

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Is Data Mining Legal?

• Data mining uses raw data from a variety of
  sources. This diversity is as diverse as the data
  mining applications. These applications include
  forecasting shoppers' behavior and financial
  services, as well as scientific research,
  engineering, and climate modeling. Data mining is
  the process of extracting useful information from
  large data sets.
• There is nothing inherently illegal about it. The
  ethical and legal gray areas are the way the
  information was obtained and used. Many of these
  data, such as road traffic movements and weather
  information, may be available in the public
  domain. It is important to understand the legal
  restrictions such as copyright laws and data
  privacy laws. Data mining insights should not be
  used for discrimination against individuals or
  groups.

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Data Mining Companies

• Data mining companies take raw data from the
  internet and standardize it. They then extract it
  in a common format, analyze it and make it
  useful. This involves obtaining data from a
  source and finding patterns, correlations, and
  trends within large data sets.
• There can be multiple steps, as explained above.
  One process can download the data, and another
  can extract some values from the HTML. The data
  can then be compiled by other processes, compared
  with previous runs, and used as an input to
  another process that will discover correlations.
• Data mining companies use a lot of automated
  methods, including AI and Machine Learning, to
  extract the relevant information from large
  amounts of data and process it into useful
  information.

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Data Mining Examples in Practice

• Data mining is a way to accurately predict,
  identify patterns, and inform forecasting. Data
  mining is used to spot gaps and errors in
  business operations. It can also be combined with
  predictive analytics and machine learning to set
  a business apart. Many businesses nowadays
  outsource web data mining services.
• Data mining techniques are used extensively in
  fields like marketing, risk management, and fraud
  detection. The ability to extract product data
  allows you to identify shopping trends and
  customer preferences.

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