02Data - Concepts and Techniques - Chapter 2 - Jiawei Han,...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon
September 16, 2009 Data Mining: Concepts and Techniques 1 Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign  Simon Fraser University ©2008 Jiawei Han, Micheline Kamber, and Jian Pei.  All  rights reserved.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
September 16, 2009 Data Mining: Concepts and Techniques 2
Background image of page 2
September 16, 2009 Data Mining: Concepts and Techniques 3 Chapter 2: Getting to Know Your Data Data Objects and Attribute Types Basic Statistical Descriptions of Data Data Visualization Measuring Data Similarity and Dissimilarity Summary
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
September 16, 2009 Data Mining: Concepts and Techniques 4 Types of Data Sets Record Relational records Data matrix, e.g., numerical matrix,  crosstabs Document data: text documents: term- frequency vector Transaction data Graph and network World Wide Web Social or information networks Molecular Structures Ordered Video data: sequence of images Temporal data: time-series Sequential Data: transaction sequences Genetic sequence data Spatial, image and multimedia: Spatial data: maps Image data:  Video data: Document 1 season timeout lost wi n game score ball pla y coach team Document 2 Document 3 3 0 5 0 2 6 0 2 0 2 0 0 7 0 2 1 0 0 3 0 0 1 0 0 1 2 2 0 3 0 TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk
Background image of page 4
September 16, 2009 Data Mining: Concepts and Techniques 5 Important Characteristics of Structured Data Dimensionality Curse of dimensionality Sparsity Only presence counts Resolution Patterns depend on the scale   Distribution Centrality and dispersion
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
September 16, 2009 Data Mining: Concepts and Techniques 6 Data Objects Data sets are made up of data objects. data object  represents an entity. Examples:  sales database:  customers, store items, sales medical database: patients, treatments university database: students, professors, courses Also called  samples , examples, instances, data points,  objects, tuples . Data objects are described by  attributes . Database rows -> data objects; columns ->attributes.
Background image of page 6
September 16, 2009 Data Mining: Concepts and Techniques 7 Attributes Attribute ( or  dimensions, features, variables ): a  data field, representing a characteristic or feature of  a data object. E.g., customer _ID, name, address Types: Nominal Binary Numeric: quantitative Interval-scaled Ratio-scaled
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
September 16, 2009 Data Mining: Concepts and Techniques 8 Attribute Types Nominal:  categories, states, or “names of things” Hair_color = {black, brown, blond, red, auburn, grey, white} marital status, occupation, ID numbers, zip codes Binary
Background image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 65

02Data - Concepts and Techniques - Chapter 2 - Jiawei Han,...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online