1 What is Data Mining? l Data mining is the process of automatically discovering useful information in large data repositories. l There are many other.

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Presentation transcript:

1 What is Data Mining? l Data mining is the process of automatically discovering useful information in large data repositories. l There are many other definitions The problem/question of interest

2 Data Mining Examples and Non-Examples Data Mining: -Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) -Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com, etc.) NOT Data Mining: -Look up phone number in phone directory -Query a Web search engine for information about “Amazon”

3 Why Mine Data? Scientific Viewpoint l Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data l Traditional techniques infeasible for raw data l Data mining may help scientists – in classifying and segmenting data – in hypothesis formation

4 Why Mine Data? Commercial Viewpoint l Lots of data is being collected and warehoused – Web data, e-commerce – Purchases at department/ grocery stores – Bank/credit card transactions l Computers have become cheaper and more powerful l Competitive pressure is strong – Provide better, customized services for an edge

5 In class exercise #1: Give an example of something you did yesterday or today which resulted in data which could potentially be mined to discover useful information.

6 Origins of Data Mining Draws ideas from machine learning, AI, pattern recognition and statistics lTraditional techniques may be unsuitable due to –Enormity of data –High dimensionality of data –Heterogeneous, distributed nature of data Statistics Data Mining AI/Machine Learning/ Pattern Recognition

7 2 Types of Data Mining Tasks l Prediction Methods: Use some variables to predict unknown or future values of other variables. l Description Methods: Find human-interpretable patterns that describe the data.

8 What is Data? l An attribute is a property or characteristic of an object l Examples: eye color of a person, temperature, etc. l Attribute is also known as variable, field, characteristic, or feature l A collection of attributes describe an object l Object is also known as record, point, case, sample, entity, instance, or observation Attributes Objects