1. What is data mining? In your answer, address the following:
\na) Is it another hype?
\nb) Is it a simple transformation or application of technology developed from databases, statistics,
\nmachine learning, and pattern recognition?
\nc) We have presented a view that data mining is the result of the evolution of database
\ntechnology. Do you think that data mining is also the result of the evolution of machine
\nlearning research? Can you present such views based on the historical progress of this
\ndiscipline? Do the same for the fields of statistics and pattern recognition.
\nd) Describe the steps (1-2 lines each step) involved in data mining when viewed as a process of
\nknowledge discovery.
\n2. How is a data warehouse different from a database? How are they similar?
\n3. Define each of the following data mining functionalities: characterization, discrimination,
\nassociation and correlation analysis, classification, regression, clustering, and outlier analysis. Give
\nexamples of each data mining functionality, using a real-life database that you are familiar with.
\n4. Present an example where data mining is crucial to the success of a business. What data mining
\nfunctionalities does this business need (e.g., think of the kinds of patterns that could be mined)?
\n5. Describe three challenges to data mining regarding data mining methodology and user interaction
\nissues.
\n6. What are the major challenges of mining a huge amount of data (such as billions of tuples) in
\ncomparison with mining a small amount of data (such as a few hundred tuple data set)?
\n7. Briefly describe the following advanced database systems and applications: object-relational
\ndatabases, spatial databases, text databases, multimedia databases, the World Wide Web.
\n8. Outliers are often discarded as noise. However, one person\u2019s garbage could be another\u2019s treasure.
\nFor example, exceptions in credit card transactions can help us detect the fraudulent use of credit
\ncards. Using fraudulence detection as an example, provide three similar examples where outliers are
\nimportant to detect.<\/p>\n","protected":false},"excerpt":{"rendered":"

1. What is data mining? In your answer, address the following: a) Is it another hype? b) Is it a simple transformation or application of technology developed from databases, statistics, machine learning, and pattern recognition? c) We have presented a view that data mining is the result of the evolution of database technology. Do you […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[64],"tags":[],"class_list":["post-7495","post","type-post","status-publish","format-standard","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/posts\/7495","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/comments?post=7495"}],"version-history":[{"count":2,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/posts\/7495\/revisions"}],"predecessor-version":[{"id":7497,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/posts\/7495\/revisions\/7497"}],"wp:attachment":[{"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/media?parent=7495"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/categories?post=7495"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/collegly.com\/wp-json\/wp\/v2\/tags?post=7495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}