statistics 202: data mining - introduction · statistics 202: data mining c jonathan taylor based...

Statistics 202:Data Mining

c©JonathanTaylor

Based inpart onslidesfromtext-book,

slides ofSusan

Holmes

Statistics 202: Data MiningIntroduction

c©Jonathan TaylorBased in part on slides from textbook, slides of Susan Holmes

October 7, 2011

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c©JonathanTaylor


slides ofSusan

Holmes

Data Mining

What is data mining?

Non-trivial extraction of implicit, previously unknown andpotentially useful information from data

Data mining involves the use of sophisticated data analysistools to discover previously unknown, valid patterns andrelationships in large data sets.

A key feature of data mining is that the data sets arelarger than those encountered in “classical” statistics. Solarge that it must be (semi-)automated.

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Data Mining

Who uses data mining?

Industry:1 Netflix2 Amazon3 Google (i.e. google trends)

Science:1 Genomics2 Climate Science3 Astrophysics4 Neuroimaging

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Netflix

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Introduction to Data Mining [Hardcover]Pang-Ning Tan (Author), Michael Steinbach (Author), VipinKumar (Author)

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Genomics

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Neuroimaging

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Climate science

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Data Mining

Some things that are not data mining

Looking up a record in a database by identifier such as lastname . (No pattern is revealed by this lookup . . . )

Searching for “Amazon” on google. (Google has donesome data mining, but you have not . . . )

Testing a two-sample hypothesis in a clinical trial. (Dataset is often not large and unstructured.)

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Data Mining

Some things that are more like data mining

Noting that some last names occur in certain geographicalareas.

Taking all query results from google on Amazon anddiscovering that there are at least two groups: “Amazonriver” and “Amazon.com”

When doing multiple tests across many different genes,identifying very strongly significant genes . . .

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Data Mining

Prediction / Supervised Problems

In such problems there is an outcome or label we want topredict based on many features.

Classification

Regression

Outlier detection

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Data Mining

Descriptive / Unsupervised Problems

In such problems, we are seeking to discover hidden “structure”in the data, without an outcome or label.

Clustering

Dimension Reduction

Association Rules

Semisupervised problems

A mix of labelled and unlabelled data is used.

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