Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes data integration and transformation through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality, handle inconsistencies, and reduce data size for mining. Techniques include binning, clustering, sampling and discretization which create intervals or concept hierarchies to generalize continuous attributes for analysis.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
Data preprocessing is important for data mining and involves data cleaning, integration, reduction, and discretization. The goals are to handle missing data, remove noise, resolve inconsistencies, reduce data size for faster mining, and prepare data for modeling. Common techniques include filling in missing values, smoothing noisy data, aggregating data, normalizing values, selecting important features, clustering data, and discretizing continuous variables. Preprocessing helps produce higher quality mining results from dirtier real-world data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and prepare data for mining algorithms through techniques like discretization and concept hierarchy generation.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data by normalization, aggregation, and reduction. The document discusses techniques for data cleaning like binning and clustering to handle noisy data. It also covers data integration, transformation through normalization, and reduction using histograms, clustering, and sampling. Discretization and concept hierarchies are introduced as techniques to reduce continuous attributes for data analysis.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Common preprocessing tasks involve handling missing data, smoothing noisy data, and integrating data from multiple sources. Techniques like normalization, attribute construction, discretization, and dimensionality reduction are presented as methods for transforming and reducing data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
Data preprocessing is important for data mining and involves data cleaning, integration, reduction, and discretization. The goals are to handle missing data, remove noise, resolve inconsistencies, reduce data size for faster mining, and prepare data for modeling. Common techniques include filling in missing values, smoothing noisy data, aggregating data, normalizing values, selecting important features, clustering data, and discretizing continuous variables. Preprocessing helps produce higher quality mining results from dirtier real-world data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and prepare data for mining algorithms through techniques like discretization and concept hierarchy generation.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data by normalization, aggregation, and reduction. The document discusses techniques for data cleaning like binning and clustering to handle noisy data. It also covers data integration, transformation through normalization, and reduction using histograms, clustering, and sampling. Discretization and concept hierarchies are introduced as techniques to reduce continuous attributes for data analysis.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Common preprocessing tasks involve handling missing data, smoothing noisy data, and integrating data from multiple sources. Techniques like normalization, attribute construction, discretization, and dimensionality reduction are presented as methods for transforming and reducing data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
This document discusses data preparation techniques for data warehousing and mining projects, including descriptive data summarization, data cleaning, integration and transformation, and reduction. It covers cleaning techniques like handling missing data, identifying outliers, and resolving inconsistencies. Data integration challenges like schema matching and resolving conflicts are also addressed. Methods for data reduction like aggregation, generalization, normalization and attribute construction are summarized.
The document discusses data preprocessing tasks that are commonly performed on real-world databases before data mining or analysis. These tasks include data cleaning to handle incomplete, noisy, or inconsistent data through techniques like filling in missing values, identifying outliers, and resolving inconsistencies. Data integration is used to combine data from multiple sources by resolving attribute name differences and eliminating redundancies. Data transformation techniques like normalization, attribute construction, aggregation, and generalization are also discussed to convert data into appropriate forms for mining algorithms or users. The goal of these preprocessing steps is to improve the quality and consistency of data for subsequent analysis and knowledge discovery.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
The document discusses various techniques for data preparation and preprocessing for data warehousing and mining projects. It covers descriptive data summarization, data cleaning, integration and transformation, and reduction. The key aspects covered include handling missing data, resolving inconsistencies, reducing redundancy through integration, and reducing data volume through techniques like aggregation, generalization and discretization while maintaining analytical capabilities. Quality data preparation is emphasized as essential for obtaining quality mining results.
This document discusses data preprocessing techniques for data mining. It covers why preprocessing is important for obtaining quality mining results from quality data. The major tasks of data preprocessing are described, including data cleaning, integration, transformation, reduction, and discretization. Specific techniques for handling missing data, noisy data, and data integration are also outlined. The goals of data reduction strategies like dimensionality and numerosity reduction are explained.
Data preprocessing involves several key steps:
1) Data cleaning to fill in missing values, identify and remove outliers, and resolve inconsistencies
2) Data integration to combine multiple data sources and resolve conflicts and redundancies
3) Data reduction techniques like discretization, dimensionality reduction, and aggregation to obtain a reduced representation of the data for mining and analysis.
Data Preprocessing can be defined as a process of converting raw data into a format that is understandable and usable for further analysis. It is an important step in the Data Preparation stage. It ensures that the outcome of the analysis is accurate, complete, and consistent.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling and analysis. The document discusses several key aspects of data preprocessing including:
- Why data preprocessing is important to improve data quality and ensure accurate analysis results.
- Common data issues like missing values, noise, inconsistencies that require cleaning. Techniques for cleaning include filling in missing data, identifying and handling outliers, and resolving inconsistencies.
- Methods for reducing data like binning, regression, clustering, sampling to obtain a smaller yet representative version of the data.
- The major tasks in preprocessing like data cleaning, integration, transformation, reduction and discretization which are aimed at handling real-world data issues.
The document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Specifically, it covers filling missing values, handling noisy data, data normalization, aggregation, attribute selection, clustering, sampling and entropy-based discretization to reduce data size while retaining important information.
The document discusses data warehousing, data mining, and business intelligence applications. It explains that data warehousing organizes and structures data for analysis, and that data mining involves preprocessing, characterization, comparison, classification, and forecasting of data to discover knowledge. The final stage is presenting discovered knowledge to end users through visualization and business intelligence applications.
This document discusses data preprocessing techniques. It explains that data is often incomplete, noisy, or inconsistent when collected from the real world. Common preprocessing steps include data cleaning to handle these issues, data integration and transformation to combine multiple data sources, and data reduction to reduce the volume of data for analysis while maintaining analytical results. Specific techniques covered include filling in missing values, identifying and smoothing outliers, resolving inconsistencies, schema integration, attribute construction, data cube aggregation, dimensionality reduction, and discretization.
Data preprocessing involves transforming raw data into a clean and understandable format. It includes data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers and resolves inconsistencies. Data integration combines data from multiple sources. Data transformation performs operations like normalization and aggregation. Data reduction obtains a reduced representation of data to improve mining performance without losing essential information.
Data preprocessing involves transforming raw data into a clean and understandable format. It includes data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers and resolves inconsistencies. Data integration combines data from multiple sources. Data transformation performs operations like normalization and aggregation. Data reduction obtains a reduced representation of data to improve mining performance without losing essential information.
The document discusses entity relationship modeling concepts including:
- Entities represent real-world objects or concepts that can exist independently
- Attributes describe entities with properties like simple or multi-valued attributes
- Relationships exist between entities when one entity's attribute refers to another's attribute
- Keys like primary keys and foreign keys are used to uniquely identify and relate entities
- Cardinality defines relationship types as one-to-one, one-to-many, or many-to-many
- Examples demonstrate applying these concepts to model systems like a university admissions system.
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Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
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Data preprocessing involves several key steps:
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Data Preprocessing can be defined as a process of converting raw data into a format that is understandable and usable for further analysis. It is an important step in the Data Preparation stage. It ensures that the outcome of the analysis is accurate, complete, and consistent.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling and analysis. The document discusses several key aspects of data preprocessing including:
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- Common data issues like missing values, noise, inconsistencies that require cleaning. Techniques for cleaning include filling in missing data, identifying and handling outliers, and resolving inconsistencies.
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The document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Specifically, it covers filling missing values, handling noisy data, data normalization, aggregation, attribute selection, clustering, sampling and entropy-based discretization to reduce data size while retaining important information.
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This document discusses data preprocessing techniques. It explains that data is often incomplete, noisy, or inconsistent when collected from the real world. Common preprocessing steps include data cleaning to handle these issues, data integration and transformation to combine multiple data sources, and data reduction to reduce the volume of data for analysis while maintaining analytical results. Specific techniques covered include filling in missing values, identifying and smoothing outliers, resolving inconsistencies, schema integration, attribute construction, data cube aggregation, dimensionality reduction, and discretization.
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
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These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
2. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
3. Why Data Preprocessing?
Data in the real world is dirty
incomplete: lacking attribute values, lacking certain
attributes of interest, or containing only aggregate
data
noisy: containing errors or outliers
inconsistent: containing discrepancies in codes or
names
No quality data, no quality mining results!
Quality decisions must be based on quality data
Data warehouse needs consistent integration of
quality data
4. Multi-Dimensional Measure of
Data Quality
A well-accepted multidimensional view:
Accuracy
Completeness
Consistency
Timeliness
Believability
Value added
Interpretability
Accessibility
5. Major Tasks in Data
Preprocessing
Data cleaning
Fill in missing values, smooth noisy data, identify or remove
outliers, and resolve inconsistencies
Data integration
Integration of multiple databases, data cubes, or files
Data transformation
Normalization and aggregation
Data reduction
Obtains reduced representation in volume but produces the
same or similar analytical results
Data discretization
Part of data reduction but with particular importance, especially
for numerical data
7. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
8. Data Cleaning
Data cleaning tasks
Fill in missing values
Identify outliers and smooth out noisy data
Correct inconsistent data
9. Missing Data
Data is not always available
E.g., many tuples have no recorded value 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
Missing data may need to be inferred.
10. How to Handle
Missing Data?
Ignore the tuple: usually done when class label is missing
(assuming the tasks in classificationโnot effective when the
percentage of missing values per attribute varies considerably)
Fill in the missing value manually: tedious + infeasible?
Use a global constant to fill in the missing value: e.g., โunknownโ, a
new class?!
Use the attribute mean to fill in the missing value
Use the most probable value to fill in the missing value: inference-
based such as Bayesian formula or decision tree
11. Noisy Data
Noise: random error or variance in a measured variable
Incorrect attribute values may due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
Other data problems which requires data cleaning
duplicate records
incomplete data
inconsistent data
12. How to Handle Noisy
Data?
Binning method:
first sort data and partition into (equi-depth) bins
then smooth by bin means, smooth by bin median,
smooth by bin boundaries, etc.
Clustering
detect and remove outliers
Combined computer and human inspection
detect suspicious values and check by human
Regression
smooth by fitting the data into regression functions
13. Simple Discretization
Methods: Binning
Equal-width (distance) partitioning:
It divides the range into N intervals of equal size: uniform grid
if A and B are the lowest and highest values of the attribute, the
width of intervals will be: W = (B-A)/N.
The most straightforward
But outliers may dominate presentation
Skewed data is not handled well.
Equal-depth (frequency) partitioning:
It divides the range into N intervals, each containing
approximately same number of samples
Good data scaling
Managing categorical attributes can be tricky.
14. Binning Methods for Data
Smoothing
* Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34
* Partition into (equi-depth) bins:
- Bin 1: 4, 8, 9, 15
- Bin 2: 21, 21, 24, 25
- Bin 3: 26, 28, 29, 34
* Smoothing by bin means:
- Bin 1: 9, 9, 9, 9
- Bin 2: 23, 23, 23, 23
- Bin 3: 29, 29, 29, 29
* Smoothing by bin boundaries:
- Bin 1: 4, 4, 4, 15
- Bin 2: 21, 21, 25, 25
- Bin 3: 26, 26, 26, 34
15. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
16. Data
Integration
Data integration:
combines data from multiple sources into a coherent
store
Schema integration
integrate metadata from different sources
Entity identification problem: identify real world entities
from multiple data sources, e.g., A.cust-id โก B.cust-#
Detecting and resolving data value conflicts
for the same real world entity, attribute values from
different sources are different
possible reasons: different representations, different
scales, e.g., metric vs. British units
17. Handling
Redundant Data
Redundant data occur often when integration of multiple
databases
The same attribute may have different names in
different databasesCareful integration of the data from
multiple sources may help reduce/avoid redundancies
and inconsistencies and improve mining speed and
quality
18. Data
Transformation
Smoothing: remove noise from data
Aggregation: summarization, data cube construction
Generalization: concept hierarchy climbing
Normalization: scaled to fall within a small, specified
range
min-max normalization
z-score normalization
normalization by decimal scaling
19. Data Transformation:
Normalization
min-max normalization
z-score normalization
normalization by decimal scaling
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20. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
21. Data Reduction
Strategies
Warehouse may store terabytes of data: Complex
data analysis/mining may take a very long time to
run on the complete data set
Data reduction
Obtains a reduced representation of the data set that is
much smaller in volume but yet produces the same (or
almost the same) analytical results
Data reduction strategies
Data cube aggregation
Dimensionality reduction
Numerosity reduction
Discretization and concept hierarchy generation
22. Data Cube Aggregation
The lowest level of a data cube
the aggregated data for an individual entity of interest
e.g., a customer in a phone calling data warehouse.
Multiple levels of aggregation in data cubes
Further reduce the size of data to deal with
Reference appropriate levels
Use the smallest representation which is enough to
solve the task
23. Dimensionality
Reduction
Feature selection (i.e., attribute subset selection):
Select a minimum set of features such that the
probability distribution of different classes given the
values for those features is as close as possible to the
original distribution given the values of all features
reduce # of patterns in the patterns, easier to understand
24. Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A1? A6?
Class 1 Class 2 Class 1 Class 2
> Reduced attribute set: {A1, A4, A6}
25. Regression and Log-
Linear Models
Linear regression: Data are modeled to fit a straight line
Often uses the least-square method to fit the line
Multiple regression: allows a response variable Y to be
modeled as a linear function of multidimensional feature
vector
Log-linear model: approximates discrete
multidimensional probability distributions
26. Linear regression: Y = ฮฑ + ฮฒ X
Two parameters , ฮฑ and ฮฒ specify the line and are to
be estimated by using the data at hand.
using the least squares criterion to the known values of
Y1, Y2, โฆ, X1, X2, โฆ.
Multiple regression: Y = b0 + b1 X1 + b2 X2.
Many nonlinear functions can be transformed into the
above.
Log-linear models:
The multi-way table of joint probabilities is
approximated by a product of lower-order tables.
Probability: p(a, b, c, d) = ฮฑab ฮฒacฯad ฮดbcd
Regress Analysis and
Log-Linear Models
27. Histograms
A popular data reduction
technique
Divide data into buckets
and store average (sum)
for each bucket
Can be constructed
optimally in one
dimension using dynamic
programming
Related to quantization
problems. 0
5
10
15
20
25
30
35
40
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
28. 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 โsmearedโ
Can have hierarchical clustering and be stored in multi-
dimensional index tree structures
There are many choices of clustering definitions and
clustering algorithms, further detailed in Chapter 8
29. Sampling
Allow a mining algorithm to run in complexity that is
potentially sub-linear to the size of the data
Choose a representative subset of the data
Simple random sampling may have very poor
performance in the presence of skew
Develop adaptive sampling methods
Stratified sampling:
Approximate the percentage of each class (or
subpopulation of interest) in the overall database
Used in conjunction with skewed data
31. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
32. Discretization
Three types of attributes:
Nominal โ values from an unordered set
Ordinal โ values from an ordered set
Continuous โ real numbers
Discretization:
divide the range of a continuous attribute into
intervals
Some classification algorithms only accept
categorical attributes.
Reduce data size by discretization
Prepare for further analysis
33. Discretization and Concept
hierachy
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).
35. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
36. Summary
Data preparation is a big issue for both warehousing
and mining
Data preparation includes
Data cleaning and data integration
Data reduction and feature selection
Discretization
A lot a methods have been developed but still an active
area of research
37. References
D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse
environments. Communications of ACM, 42:73-78, 1999.
Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of
the Technical Committee on Data Engineering, 20(4), December 1997.
D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999.
T. Redman. Data Quality: Management and Technology. Bantam Books,
New York, 1992.
Y. Wand and R. Wang. Anchoring data quality dimensions ontological
foundations. Communications of ACM, 39:86-95, 1996.
R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality
research. IEEE Trans. Knowledge and Data Engineering, 7:623-640,
1995.