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Give examples of data mining applications
in Data Science & Statistics by Platinum (142,760 points) | 190 views

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In the early 1990s the phrase 'data mining' became popular. Currently statistical learning, data analytics, data science are the other commonly used terms. Since data has become very cheap and data collection methods almost automated, in many fields, such as business domain, success depends on efficient and intelligent utilization of collected data. In this respect data mining efforts are omnipresent. Following examples are only indicative of a few interesting application areas. As more communication between different disciplines occurs, application areas are likely to evolve, and new ones to emerge.

  • Businesses benefit from data mining
    • Large retailers like Walmart utilize information on store footfall, advertising campaign and even weather forecast to predict sales and stock up accordingly.
    • Credit card companies mine transaction records for fraudulent use of their cards based on purchase patterns of consumers - They can deny access if your purchase patterns change drastically!
  • In Genomics research gathers speed using computational methods
    • The Human Genome Project mounts up piles of data but getting the data to work for humankind to develop new drug and weed out diseases, will require pattern recognition in the data which is handled in bioinformatics.
    • Scientists use Microarray data to look at the gene expressions and sophisticated data analysis techniques are employed to account for the background noise and normalization of data.
  • Information retrieval
    • Terabytes of data are being cumulated on the internet which includes Facebook and Twitter data as well as Instagrams and other social networking sites. This vast repository may be mined, and controlled to some extent, to swerve public opinion in a candidate's favor (election strategy) or evaluate a product's performance (marketing and sales strategy)
    • Another aspect of social media is the Multimedia information containing the visual as well as audio data files. How do we manage these types of data efficiently? Mining non-alphanumeric data is not easy.
  • Communication systems
    • Speech recognition is one area where important pattern recognistion methods were developed and have been transferred to other application areas.
    • Image analysis is another important area of data mining application and facial recognition techniques are a part of security arrangements
by Platinum (142,760 points)

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