The document discusses the importance of data preprocessing. It explains that real-world data is often dirty, incomplete, noisy, or inconsistent. The major tasks in data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning aims to fill in missing values, identify outliers, and resolve inconsistencies. Data integration combines data from multiple sources which poses issues like schema and entity identification. Other techniques discussed are normalization, aggregation, sampling, clustering, and using concept hierarchies to reduce data dimensionality.

2. WHY DATA PREPROCESSING?
Data in the real world is dirty
 incomplete: missing attribute values, lack of certain
attributes of interest, or containing only aggregate
data
 e.g., occupation=“”
 noisy: containing errors or outliers
 e.g., Salary=“-10”
 inconsistent: containing discrepancies in codes or
names
 e.g., Age=“42” Birthday=“03/07/1997”
 e.g., Was rating “1,2,3”, now rating “A, B, C”
 e.g., discrepancy between duplicate records

3. MAJOR TASKS IN DATA PREPROCESSING
Data cleaning
 Fill in missing values, smooth noisy data, identify or remove
outliers and noisy data, and resolve inconsistencies
Data integration
 Integration of multiple databases, or files
Data transformation
 Normalization and aggregation
Data reduction
 Obtains reduced representation in volume but produces the same
or similar analytical results
Data discretization (for numerical data)

4. DATA CLEANING
Importance
 “Data cleaning is the number one problem in data
warehousing”
Data cleaning tasks – this routine attempts to
 Fill in missing values
 Identify outliers and smooth out noisy data
 Correct inconsistent data
 Resolve redundancy caused by data integration

5. MISSING DATA
Data is not always available
 E.g., many tuples have no recorded values for several
attributes, such as customer income in sales data
Missing data may be due to
 equipment malfunction
 inconsistent with other recorded data and thus deleted
 data not entered due to misunderstanding
 certain data may not be considered important at the time of
entry
 not register history or changes of the data

6. DATA INTEGRATION
Data integration:
 combines data from multiple sources(data cubes, multiple db or
flat files)
Issues during data integration
 Schema integration
 integrate metadata (about the data) from different sources
 Entity identification problem: identify real world entities from
multiple data sources, e.g., A.cust-id B.cust-#(same entity?)
 Detecting and resolving data value conflicts
 for the same real world entity, attribute values from different
sources are different, e.g., different scales, metric vs. British
units
 Removing duplicates and redundant data
 An attribute can be derived from another table (annual revenue)
 Inconsistencies in attribute naming

7. DATA TRANSFORMATION
Smoothing: remove noise from data
(binning, clustering, regression)
Normalization: scaled to fall within a small, specified range
such as –1.0 to 1.0 or 0.0 to 1.0
Attribute/feature construction
 New attributes constructed / added from the given ones
Aggregation: summarization or aggregation operations
apply to data
Generalization: concept hierarchy climbing
 Low level/ primitive/raw data are replace by higher level
concepts

8. DATA REDUCTION STRATEGIES
Data is too big to work with – may takes
time, impractical or infeasible analysis
Data reduction techniques
 Obtain a reduced representation of the data set that
is much smaller in volume but yet produce the same
(or almost the same) analytical results
Data reduction strategies
 Data cube aggregation – apply aggregation
operations (data cube)

9. CLUSTERING
Partition data set into clusters, and one can store cluster representation
only
Can be very effective if data is clustered but not if data is “smeared”/
spread
There are many choices of clustering definitions and clustering algorithms.
We will discuss them later.

10. SAMPLING
Data reduction technique
 A large data set to be represented by much smaller
random sample or subset.
4 types
 Simple random sampling without replacement
(SRSWOR).
 Simple random sampling with replacement
(SRSWR).
 Develop adaptive sampling methods such as cluster
sample and stratified sample

11. DISCRETIZATION AND CONCEPT HIERARCHY
Discretization
 reduce the number of values for a given continuous
attribute by dividing the range of the attribute into
intervals. Interval labels can then be used to replace
actual data values
Concept hierarchies
 reduce the data by collecting and replacing low level
concepts (such as numeric values for the attribute age)
by higher level concepts (such as young, middle-aged, or
senior)

12. SOME TECHNIQUES
-Binning methods – equal-width, equal-frequency
-Histogram
- Entropy-based methods

13. SUMMARY
Data preparation is a big issue for data
mining
Data preparation includes
 Data cleaning and data integration
 Data reduction and feature selection
 Discretization
Many methods have been proposed but
still an active area of research