Data Representation Information Retrieval and Data Mining. Prof. Matteo Matteucci
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1 Data Representation Information Retrieval and Data Mining Prof. Matteo Matteucci
2 Instances, Attributes, Concepts 2 Instances The atomic elements of information from a dataset Also known as records, prototypes, or examples Attributes Measures aspects of an instance Also known as features or variables Each instance is composed of a certain number of attributes Concepts Special content inside the data Kind of things that can be learned
3 Contact Lenses Data 3 Pre-presbyopic rmal Pre-presbyopic Presbyopic rmal Presbyopic Presbyopic Hard rmal Presbyopic Presbyopic Soft rmal Presbyopic Presbyopic rmal Presbyopic Soft rmal Pre-presbyopic Pre-presbyopic Hard rmal Pre-presbyopic Pre-presbyopic Soft rmal Pre-presbyopic Pre-presbyopic hard rmal Young Young Soft rmal Young Young Hard rmal Young Young Soft rmal Young Young Recommended lenses Tear production rate Astigmatism Spectacle prescription Age
4 CPU Performance Data 4 Cycle time (ns) Main memory (Kb) Cache (Kb) Channels Performance MYCT MMIN MMAX CACH CHMIN CHMAX PRP
5 Two Versions of the Weather Data 5 Outlook Temperature Humidity Windy Play Sunny Hot High False Sunny Hot High True Overcast Hot High False Rainy Mild rmal False Outlook Temperature Humidity Windy Play Sunny False Sunny True Overcast False Rainy False
6 Attribute Types
7 minal Attributes 7 minal Attributes (a.k.a. Discrete Attributes) Values are distinct symbols Values themselves serve only as labels or names Example Attribute outlook from weather data Values: sunny, overcast, and rainy Characteristics relation is implied among nominal values ordering distance measure Only equality tests can be performed
8 Ordinal Attributes 8 Ordinal Attributes Impose order on values distance between values defined Example The attribute temperature in weather data Values: hot > mild > cool Characteristics Addition and subtraction don t make sense Distinction between nominal and ordinal not always clear (e.g. attribute outlook ) Some sort of distance could be thought
9 Contact Lens Age: minal or Ordinal? 9 Age Young Young Young Young Young Young Young Young Pre-presbyopic Pre-presbyopic Pre-presbyopic Pre-presbyopic Pre-presbyopic Pre-presbyopic Pre-presbyopic Pre-presbyopic Presbyopic Presbyopic Presbyopic Presbyopic Presbyopic Presbyopic Presbyopic Presbyopic Spectacle prescription Astigmatism Tear production rate rmal rmal rmal rmal rmal rmal rmal rmal rmal rmal rmal rmal Recommended lenses Soft Hard Soft hard Soft Hard Soft Hard Soft
10 minal or Ordinal? 10 Attribute age nominal If age = young and astigmatic = no and tear production rate = normal then recommendation = soft Attribute age ordinal (e.g. young < pre-presbyopic < presbyopic ) If age pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft
11 Interval/Numerical Attributes 11 Interval/Numerical Attributes t only ordered but measured in fixed and equal units Examples Attribute temperature expressed in degrees Attribute year Characteristics Difference of two values makes sense Sum or product doesn t make sense Zero point is not defined
12 Ratio Attributes 12 Ratio Attributes Ratio quantities are ones for which the measurement scheme defines a zero point Example Attribute distance Characteristics Distance between an object and itself is zero Ratio quantities are treated as real numbers All mathematical operations are allowed Is there an inherently defined zero point? It depends on scientific knowledge
13 What Attribute Types in Practice? 13 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. te: 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 floatingpoint variables.
14 Why Specifying Attribute Types? 14 Check for valid values Express the best possible patterns into data Make the most adequate comparisons Example Outlook > sunny does not make sense, while Temperature > cool or Humidity > 70 does Additional uses of attribute type Deal with missing values
15 Missing Values
16 Why Missing Values Exist? 16 Faulty equipment, incorrect measurements, missing cells in manual data entry, censored/anonymous data Very frequent in questionnaires for medical scenarios Censored/anonymous data In practice, a low rate of missing values may be suspicious Interview data provide many examples For whom will you cast your vote in the next election? What is your salary? Did you ever?
17 Missing Values 17 Frequently indicated by out-of-range entries Missing value may have significance in itself E.g. missing test in a medical examination Most schemes assume that is not the case missing may need to be coded as additional value Does absence of value have some significance? If it does, missing is a separate value If it does not, missing must be treated in a special way
18 What Types of Missing Values? 18 Missing completely at random (MCAR): when the distribution of an example having a missing value for an attribute does not depend on either the observed data or the missing data Missing at random (MAR), when the distribution of an example having a missing value for an attribute depends on the observed data, but does not depend on the missing data t missing at random (NMAR), when the distribution of an example having a missing value for an attribute depends on the missing values. The handling of missing data depends on the type
19 Strategies for missing values handling 19 Discarding examples with missing values Simplest approach Allows the use of unmodified data mining methods Only practical if there are few examples with missing values. Otherwise, it can introduce bias Convert the missing values into a new value Use a special value for it Add an attribute that indicates if value is missing or not Greatly increases the difficulty of the data minig process Imputation methods Assign a value to the missing one, based on the dataset. Use the unmodified data mining methods.
20 Imputation methods 20 Extract a model from the dataset to perform the imputation E.g., Use most common/frequent values Suitable for MCAR and, to a lesser extent, for MAR t suitable for NMAR type of missing data For NMAR we need to go back to the source of the data to obtain more information Survey of imputation methods available at
21 Inaccurate Values 21 Data has not been collected for mining it Errors and omissions that don t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes, thus values need to be checked for consistency Typographical and measurement errors in numeric attributes, thus outliers need to be identified Errors may be deliberate (e.g., wrong zip codes or phone numbers) Life is hard!
22 Data Formats
23 Data Format 23 Most commercial tools have their own proprietary format Most tools import excel files and comma-separated value files Year,Make,Model,Length 1997,Ford,E350, ,Mercury,Cougar,2.38 Year;Make;Model;Length 1997;Ford;E350;2, ;Mercury;Cougar;2,38
24 Attribute-Relation File Format (ARFF) 24 % % ARFF file for weather data with some numeric features outlook {sunny, overcast, temperature humidity windy {true, play? {yes, sunny, 85, 85, false, no sunny, 80, 90, true, no overcast, 83, 86, false, yes...
25 Additional Attribute Types 25 ARFF supports string description string Similar to nominal attributes but list of values is not pre-specified ARFF also supports date today date Uses the ISO-8601 combined date and time format yyyy-mm-dd-thh:mm:ss
26 Missing Values in ARFF 'duration' 'wage-increase-first-year' 'wage-increase-second-year' 'wage-increase-third-year' 'cost-of-living-adjustment' 'working-hours' 'pension' 'standby-pay' 'shift-differential' 'education-allowance' 'statutory-holidays' 'vacation' 'longterm-disability-assistance' 'contribution-to-dental-plan' 'bereavement-assistance' 'contribution-to-health-plan' 'class' 1,5,?,?,?,40,?,?,2,?,11,'average',?,?,'yes',?,'good' 2,4.5,5.8,?,?,35,'ret_allw',?,?,'yes',11,'below_average',?,'full',?,'full','good'?,?,?,?,?,38,'empl_contr',?,5,?,11,'generous','yes','half','yes','half','good' 3,3.7,4,5,'tc',?,?,?,?,'yes',?,?,?,?,'yes',?,'good'
27 Attribute Types and Interpretation 27 Interpretation of attribute types in ARFF depends on the mining scheme Numeric attributes are interpreted as Ordinal scales if less-than and greater-than are used Ratio scales if distance calculations are performed (normalization/standardization may be required) Instance-based schemes define distance between nominal values (0 if values are equal, 1 otherwise) Integers in some given data file: nominal, ordinal, or ratio scale?
28 DSPL: Dataset Publishing Language 28 Open format by Google available at Use existing data: add an XML metadata file to existing CSV Read by the Google Public Data Explorer, which includes animated bar chart, motion chart, and map visualization Allow linking to concepts in other datasets Geo-enabled: allows adding latitude and longitude data to your concept definitions
29 Predictive Model Markup Language 29 XML-based markup language developed by the Data Mining Group (DMG) to provide a way for applications to define models related to predictive analytics and data mining The goal is to share models between applications Vendor-independent method of defining models Allow to exchange of models between applications. PMML Components: data dictionary, data transformations, model, mining schema, targets, output
30 Data Repository
31 Publicly Available Datasets 31 UCI repository Probably the most famous collection of datasets Kaggle It is not a static repository of datasets, but a site that manages Data Mining competitions Example of the modern concept of crowdsourcing
32 Publicly Available Datasets 32 KDNuggets PSPbenchmarks Datasets derived from Protein Structure Prediction problems Interesting benchmarks because they can be parameterized in a very large variety of ways Pascal Large Scale Learning Challenge
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