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Data Mining: Data

Data Mining: Data
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Published Date:22-07-2017
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Data Mining: Data © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›What is Data?  Collection of data objects and Attributes their attributes Tid Refund Marital Taxable  An attribute is a property or Cheat Status Income characteristic of an object 1 Yes Single 125K No – Examples: eye color of a 2 No Married 100K No person, temperature, etc. 3 No Single 70K No – Attribute is also known as 4 Yes Married 120K No variable, field, characteristic, 5 No Divorced 95K Yes or feature Objects 6 No Married 60K No  A collection of attributes 7 Yes Divorced 220K No describe an object 8 No Single 85K Yes – Object is also known as 9 No Married 75K No record, point, case, sample, 10 No Single 90K Yes entity, or instance 10 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Attribute Values  Attribute values are numbers or symbols assigned to an attribute  Distinction between attributes and attribute values – Same attribute can be mapped to different attribute values  Example: height can be measured in feet or meters – Different attributes can be mapped to the same set of values  Example: Attribute values for ID and age are integers  But properties of attribute values can be different – ID has no limit but age has a maximum and minimum value © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Measurement of Length  The way you measure an attribute is somewhat may not match the attributes properties. A 5 1 B 7 2 C 8 3 D 10 4 E 15 5 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Types of Attributes  There are different types of attributes – Nominal  Examples: ID numbers, eye color, zip codes – Ordinal  Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in tall, medium, short – Interval  Examples: calendar dates, temperatures in Celsius or Fahrenheit. – Ratio  Examples: temperature in Kelvin, length, time, counts © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Properties of Attribute Values  The type of an attribute depends on which of the following properties it possesses: – Distinctness: =  – Order: – Addition: + - – Multiplication: / – Nominal attribute: distinctness – Ordinal attribute: distinctness & order – Interval attribute: distinctness, order & addition – Ratio attribute: all 4 properties © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Attribute Description Examples Operations Type Nominal The values of a nominal attribute are zip codes, employee mode, entropy, just different names, i.e., nominal ID numbers, eye color, contingency 2 attributes provide only enough sex: male, female correlation,  test information to distinguish one object from another. (=, ) Ordinal The values of an ordinal attribute hardness of minerals, median, percentiles, provide enough information to order good, better, best, rank correlation, objects. (, ) grades, street numbers run tests, sign tests Interval For interval attributes, the calendar dates, mean, standard differences between values are temperature in Celsius deviation, Pearson's meaningful, i.e., a unit of or Fahrenheit correlation, t and F measurement exists. tests (+, - ) Ratio For ratio variables, both differences temperature in Kelvin, geometric mean, and ratios are meaningful. (, /) monetary quantities, harmonic mean, counts, age, mass, percent variation length, electrical currentAttribute Transformation Comments Level Nominal Any permutation of values If all employee ID numbers were reassigned, would it make any difference? Ordinal An order preserving change of An attribute encompassing values, i.e., the notion of good, better new_value = f(old_value) best can be represented where f is a monotonic function. equally well by the values 1, 2, 3 or by 0.5, 1, 10. Interval new_value =a old_value + b Thus, the Fahrenheit and where a and b are constants Celsius temperature scales differ in terms of where their zero value is and the size of a unit (degree). Ratio new_value = a old_value Length can be measured in meters or feet.Discrete and Continuous Attributes  Discrete Attribute – Has only a finite or countably infinite set of values – Examples: zip codes, counts, or the set of words in a collection of documents – Often represented as integer variables. – Note: binary attributes are a special case of discrete attributes  Continuous Attribute – Has real numbers as attribute values – Examples: temperature, height, or weight. – Practically, real values can only be measured and represented using a finite number of digits. – Continuous attributes are typically represented as floating-point variables. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Types of data sets  Record – Data Matrix – Document Data – Transaction Data  Graph – World Wide Web – Molecular Structures  Ordered – Spatial Data – Temporal Data – Sequential Data – Genetic Sequence Data © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Important Characteristics of Structured Data – Dimensionality  Curse of Dimensionality – Sparsity  Only presence counts – Resolution  Patterns depend on the scale © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Record Data  Data that consists of a collection of records, each of which consists of a fixed set of attributes Tid Refund Marital Taxable Cheat Status Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Data Matrix  If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multi-dimensional space, where each dimension represents a distinct attribute  Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute P Pr ro ojje ec ct tiio on n P Pr ro ojje ec ct tiio on n D Diis st ta an nc ce e L Lo oa ad d T Th hiic ck kn ne es ss s o of f x x L Lo oa ad d o of f y y llo oa ad d 1 10 0..2 23 3 5 5..2 27 7 1 15 5..2 22 2 2 2..7 7 1 1..2 2 1 12 2..6 65 5 6 6..2 25 5 1 16 6..2 22 2 2 2..2 2 1 1..1 1 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›season timeout lost wi n game score ball pla y coach team Document Data  Each document becomes a `term' vector, – each term is a component (attribute) of the vector, – the value of each component is the number of times the corresponding term occurs in the document. Document 1 3 0 5 0 2 6 0 2 0 2 Document 2 0 7 0 2 1 0 0 3 0 0 Document 3 0 1 0 0 1 2 2 0 3 0 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Transaction Data  A special type of record data, where – each record (transaction) involves a set of items. – For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items. TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Graph Data  Examples: Generic graph and HTML Links a href="papers/papers.htmlbbbb" Data Mining /a li a href="papers/papers.htmlaaaa" 2 Graph Partitioning /a li a href="papers/papers.htmlaaaa" 1 5 Parallel Solution of Sparse Linear System of Equations /a li 2 a href="papers/papers.htmlffff" N-Body Computation and Dense Linear System Solvers 5 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Chemical Data  Benzene Molecule: C H 6 6 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Ordered Data  Sequences of transactions Items/Events An element of the sequence © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Ordered Data  Genomic sequence data GGTTCCGCCTTCAGCCCCGCGCC CGCAGGGCCCGCCCCGCGCCGTC GAGAAGGGCCCGCCTGGCGGGCG GGGGGAGGCGGGGCCGCCCGAGC CCAACCGAGTCCGACCAGGTGCC CCCTCTGCTCGGCCTAGACCTGA GCTCATTAGGCGGCAGCGGACAG GCCAAGTAGAACACGCGAAGCGC TGGGCTGCCTGCTGCGACCAGGG © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Ordered Data  Spatio-Temporal Data Average Monthly Temperature of land and ocean © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›