CS4323 Data Mining Data and Data Preprocessing

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CS4323 Data MiningData and Data Preprocessing
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DATA and DATA PREPROCESSING
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What is Data?
Attributes

• Attribute is also known as variable, field,
  characteristic, or feature
• Object is also known as record, point, case,
  sample, entity, or instance

Objects
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Whats in an attribute?

• Each instance is described by a fixed predefined
  set of features, its attributes
• Possible attribute types (levels of
  measurement)
• Nominal, ordinal, interval and ratio

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Nominal quantities

• Values are distinct symbols
• Values themselves serve only as labels or names
• Nominal comes from the Latin word for name
• Example attribute outlook from weather data
• Values sunny,overcast, and rainy
• No relation is implied among nominal values (no
  ordering or distance measure)
• Only equality tests can be performed

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Ordinal quantities

• Impose order on values
• But no distance between values defined
• Exampleattribute temperature in weather data
• Values hot gt mild gt cool
• Note addition and subtraction dont make sense
• Example rule temperature lt hot c play yes

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Interval quantities (Numeric)

• Interval quantities are not only ordered but
  measured in fixed and equal units
• Example 1 attribute temperature expressed in
  degrees Fahrenheit
• Example 2 attribute year
• Difference of two values makes sense
• Sum or product doesnt make sense

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Ratio quantities

• Example attribute distance
• Distance between an object and itself is zero
• Ratio quantities are treated as real numbers
• All mathematical operations are allowed

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Attribute types used in practice

• Most schemes accommodate just two levels of
  measurement nominal and ordinal
• Nominal attributes are also called categorical,
  enumerated, or discrete
• But enumerated and discrete imply order
• Special case dichotomy (boolean attribute)
• Ordinal attributes are called numeric, or
  continuous

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Attribute types Summary

• Nominal, e.g. eye colorbrown, blue,
• only equality tests
• important special case boolean (True/False)
• Ordinal, e.g. gradek,1,2,..,12
• Continuous (numeric), e.g. year
• interval quantities integer
• ratio quantities -- real

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Why specify attribute types?

• Q Why Machine Learning algorithms need to know
  about attribute type?
• A To be able to make right comparisons and learn
  correct concepts, e.g.
• Outlook gt sunny does not make sense, while
• Temperature gt cool or
• Humidity gt 70 does
• Additional uses of attribute type check for
  valid values, deal with missing, etc.

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Transforming ordinal to boolean

• Simple transformation allowsordinal attribute
  with n valuesto be coded using n1 boolean
  attributes
• Example attribute temperature
• Better than coding it as a nominal attribute

Original data
Transformed data
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The ARFF format
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Attribute types in Weka

• ARFF supports numeric and nominal attributes
• Interpretation depends on learning 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)

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Nominal vs. ordinal

• Attribute age nominal
• Attribute age ordinal
• (e.g. young lt pre-presbyopic lt
  presbyopic)

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• APA SAJA TIPE ATRIBUT
• DI WEKA
• DAN
• DI CLEMENTINE?

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Types of data sets

• Record
• Data Matrix
• Document Data
• Transaction Data
• Graph
• World Wide Web
• Molecular Structures
• Ordered
• Spatial Data
• Temporal Data
• Sequential Data

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Record Data

• Data that consists of a collection of records,
  each of which consists of a fixed set of
  attributes

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Document Data

• Each document becomes a term' vector

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Transaction Data

• A special type of record data, where each record
  (transaction) involves a set of items.

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Graph Data

• Examples Generic graph and HTML Links

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Ordered Data

• Sequences of transactions

Items/Events
An element of the sequence
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Ordered Data

• Genomic sequence data

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Data Preprocessing

• Aggregation
• Sampling (.. Oversampling, undersampling)
• Dimensionality Reduction
• Feature subset selection
• Feature creation
• Discretization and Binarization
• Attribute Transformation

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Curse of Dimensionality

• When dimensionality increases, data becomes
  increasingly sparse in the space that it occupies
• Definitions of density and distance between
  points, which is critical for clustering and
  outlier detection, become less meaningful

• Randomly generate 500 points
• Compute difference between max and min distance
  between any pair of points

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• BAGAIMANA KEMAMPUAN VISUALISASI PADA WEKA
• DAN
• PADA CLEMENTINE?

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DATA EXPLORATION
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What is data exploration?
A preliminary exploration of the data to better
understand its characteristics.

• Key motivations of data exploration include
• Helping to select the right tool for
  preprocessing or analysis
• Making use of humans abilities to recognize
  patterns
• People can recognize patterns not captured by
  data analysis tools
• Related to the area of Exploratory Data Analysis
  (EDA)
• How we dissect a data set what we look for how
  we look and how we interpret
• EDA heavily uses the collection of techniques
  that we call "statistical graphics"

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Visualization

• Visualization is the conversion of data into a
  visual or tabular format so that the
  characteristics of the data and the relationships
  among data items or attributes can be analyzed or
  reported.
• Visualization of data is one of the most powerful
  and appealing techniques for data exploration.
• Can detect general patterns and trends
• Can detect outliers and unusual patterns

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Visualization Techniques Histograms

• Histogram
• Usually shows the distribution of values of a
  single variable
• Example Petal Width (10 and 20 bins,
  respectively)

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Two-Dimensional Histograms

• Show the joint distribution of the values of two
  attributes
• Example petal width and petal length
• What does this tell us?

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Visualization Techniques Box Plots

• Box Plots

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Example of Box Plots

• Box plots can be used to compare attributes

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Scatter Plot Array of Iris Attributes
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Contour Plot Example SST Dec, 1998
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Visualization of the Iris Correlation Matrix
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Parallel Coordinates Plots for Iris Data
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OLAP

• Relational databases put data into tables, while
  OLAP uses a multidimensional array
  representation.
• Such representations of data previously existed
  in statistics and other fields
• There are a number of data analysis and data
  exploration operations that are easier with such
  a data representation.

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Example Iris data (continued)

• Each unique tuple of petal width, petal length,
  and species type identifies one element of the
  array.
• This element is assigned the corresponding count
  value.
• The figure illustrates the result.
• All non-specified tuples are 0.