The Data Explosion
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Questions and Answers

What is the estimated daily volume of data generated by NASA's current Earth observation satellites?

  • 100 exabytes
  • 1 terabyte (correct)
  • 100 gigabytes
  • 10 petabytes

    • Approximately how many users are there on Facebook?

  • 100 million
  • 900 million (correct)
  • 500 million
  • 1.5 billion

    • What is the estimated number of tweets sent daily on Twitter?

  • 350 million (correct)
  • 100 million
  • 500 million
  • 200 million

    • What is the estimated number of websites?

    <p>650 million</p> Signup and view all the answers

    What type of data is recorded by CCTV recordings?

    <p>Non-symbolic data</p> Signup and view all the answers

    What is the purpose of a Data Warehouse?

    <p>To store and analyze customer transactions</p> Signup and view all the answers

    What is a consequence of the vast amounts of data being stored?

    <p>Most of the data is not examined in detail.</p> Signup and view all the answers

    What is the potential of machine learning technology?

    <p>To solve the problem of the tidal wave of data.</p> Signup and view all the answers

    What is the goal of knowledge discovery?

    <p>To extract implicit, previously unknown and potentially useful information from data.</p> Signup and view all the answers

    What is the role of data mining in knowledge discovery?

    <p>It is a central part of the knowledge discovery process.</p> Signup and view all the answers

    What is the outcome of the knowledge discovery process?

    <p>New and potentially useful knowledge.</p> Signup and view all the answers

    What happens to most of the data that is stored?

    <p>It is merely stored and never examined.</p> Signup and view all the answers

    What is the current state of the world in terms of data and knowledge?

    <p>Data rich but knowledge poor.</p> Signup and view all the answers

    What is a potential application of knowledge discovery?

    <p>All of the above.</p> Signup and view all the answers

    What is the primary goal of using labelled data in data mining?

    <p>To predict the value of a designated attribute for unseen instances</p> Signup and view all the answers

    What is the term for data mining using unlabelled data?

    <p>Unsupervised learning</p> Signup and view all the answers

    What is the task called when the designated attribute is categorical?

    <p>Classification</p> Signup and view all the answers

    What is the term for a dataset of examples, each comprising the values of a number of variables?

    <p>Instances</p> Signup and view all the answers

    What is the goal of data mining when using unlabelled data?

    <p>To extract the most information from the data available</p> Signup and view all the answers

    What is the term for the process of predicting a numerical outcome?

    <p>Regression</p> Signup and view all the answers

    What is the primary goal of classification in data mining?

    <p>To predict the value of a categorical attribute</p> Signup and view all the answers

    What is the term for data that has a specially designated attribute?

    <p>Labelled data</p> Signup and view all the answers

    What is the goal of the analysis in the given dataset?

    <p>To predict the degree classification for other students given their grade profiles</p> Signup and view all the answers

    What method involves identifying the closest examples to an unclassified instance?

    <p>Nearest Neighbour Matching</p> Signup and view all the answers

    What is the purpose of a classification tree?

    <p>To generate classification rules</p> Signup and view all the answers

    What type of structure is used to generate classification rules?

    <p>Decision Tree</p> Signup and view all the answers

    What is the form of the dataset?

    <p>A table containing students' grades on five subjects</p> Signup and view all the answers

    What is the purpose of the classification rules?

    <p>To predict the degree classification of an unseen instance</p> Signup and view all the answers

    What is the result of applying the nearest neighbour matching method?

    <p>A predicted degree classification for an unseen instance</p> Signup and view all the answers

    What is the relationship between the attributes in the dataset?

    <p>The attributes are used to predict the degree classification</p> Signup and view all the answers

    What is the primary goal of market basket analysis?

    <p>To find relationships between product purchases</p> Signup and view all the answers

    What is the purpose of stating association rules with additional information?

    <p>To indicate the reliability of the rules</p> Signup and view all the answers

    What is the main difference between supervised and unsupervised learning?

    <p>The presence of labeled data</p> Signup and view all the answers

    What is the purpose of clustering algorithms?

    <p>To find groups of similar items</p> Signup and view all the answers

    What is an example of a clustering application?

    <p>Fault diagnosis</p> Signup and view all the answers

    What is the concept of 'IF variable 1 > 85 and switch 6 = open THEN variable 23 < 47.5 and switch 8 = closed (probability = 0.8)' an example of?

    <p>Association rule</p> Signup and view all the answers

    What is the term for the type of prediction where the value to be predicted is a label?

    <p>Classification</p> Signup and view all the answers

    What is the term for the process of finding relationships between product purchases?

    <p>Market basket analysis</p> Signup and view all the answers

    Study Notes

    The Data Explosion

    • Modern computer systems are accumulating data at an unimaginable rate from a wide variety of sources, including point-of-sale machines, machines logging cheque clearance, bank cash withdrawals, credit card transactions, and Earth observation satellites.
    • The volume of data is enormous, with examples including:
      • NASA Earth observation satellites generating a terabyte (10^9 bytes) of data every day.
      • The Human Genome project storing thousands of bytes for each of several billion genetic bases.
      • Data warehouses containing over a hundred million customer transactions.
      • Automatic recording devices, such as credit card transaction files and web logs, as well as non-symbolic data such as CCTV recordings.
      • Over 650 million websites, with some extremely large sites.
      • Over 900 million Facebook users, with an estimated 3 billion postings a day, and 150 million Twitter users, sending 350 million tweets a day.

    Knowledge Discovery

    • Knowledge Discovery is the non-trivial extraction of implicit, previously unknown, and potentially useful information from data.
    • It involves a process of data mining, which is a central part of the Knowledge Discovery process.
    • The Knowledge Discovery process involves:
      • Data coming in from many sources.
      • Data integration and storage in a common data store.
      • Pre-processing of data into a standard format.
      • Applying a data mining algorithm to produce rules or patterns.
      • Interpreting the output to gain new and potentially useful knowledge.

    Types of Data and Data Mining

    • There are two types of data: labelled and unlabelled data.
    • Labelled data is used for supervised learning, where the aim is to predict the value of a designated attribute for unseen instances.
    • Unlabelled data is used for unsupervised learning, where the aim is to extract the most information possible from the available data.
    • Data mining applications can be divided into four main types:
      • Classification: predicting a categorical value, such as classifying medical patients into high, medium, or low risk of acquiring an illness.
      • Numerical Prediction: predicting a numerical value, such as the expected sale price of a house.
      • Association: finding relationships amongst variables, such as in market basket analysis.
      • Clustering: grouping items that are similar, such as customers according to income, age, and types of policy purchased.

    Classification

    • Classification is a common application of data mining, involving predicting a categorical value.
    • Examples include:
      • Classifying medical patients into high, medium, or low risk of acquiring an illness.
      • Classifying people into those likely to vote for different political parties.
      • Classifying student projects into distinction, merit, pass, or fail.

    Association Rules

    • Association rules involve finding relationships amongst variables, such as in market basket analysis.
    • An example of an association rule is: IF cheese AND milk THEN bread (probability = 0.7), indicating that 70% of customers who buy cheese and milk also buy bread.

    Clustering

    • Clustering algorithms examine data to find groups of items that are similar.
    • Examples include:
      • Grouping customers according to income, age, and types of policy purchased.
      • Grouping electrical faults according to the values of certain key variables.

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    Description

    Explore the rapid accumulation of data in modern computer systems from various sources, including point-of-sale machines, bank transactions, and earth observation satellites.

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