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4/7/2003 Data Mining: Concepts and Techniques 1
Data Mining:
Concepts and Techniques
ó Slides for Textbook ó
ó Chapter 3 ó
©Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca 4/7/2003 Data Mining: Concepts and Techniques 2
Data Pre-processing
! Last of the ìintroductoryî lecture
! HW due on Wednesday
! Next lecture:
! Data mining tasks and algorithms:
classification methods
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Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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Why Data Preprocessing?
! Data in the real world is-- ! 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
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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 4/7/2003 Data Mining: Concepts and Techniques 6
Forms of data preprocessing
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Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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Data Cleaning
! Data cleaning tasks
! Fill in missing values
! Identify outliers and smooth out noisy data
! Correct inconsistent data
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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 ! 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.
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How to Handle Missing Data?
! Ignore the tuple: usually done when class label is missing (assuming the tasks in classificationónot 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., ìunknownî, a new class?! ! Use the attribute mean to fill in the missing value ! Use the attribute mean for all samples belonging to the same class to fill in the missing value: smarter/better? ! Use the most probable value to fill in the missing value: inference- based such as Bayesian formula or decision tree
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Noisy Data
! Noise: random error or variance in a measured variable ! Incorrect attribute values may be 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
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How to Handle Noisy Data?
! Binning method: ! first sort data and partition into (equi-depth) bins ! then one can 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
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Cluster Analysis
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Regression
x
y
y = x + 1
X
Y
Y1í
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Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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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 ≡ 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
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Handling Redundant Data
in Data Integration
! Redundant data occur often during integration of multiple databases ! The same attribute may have different names in different databases ! One attribute may be a ìderivedî attribute in another table, e.g., annual revenue ! Redundant data may be able to be detected by correlational analysis ! Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality 4/7/2003 Data Mining: Concepts and Techniques 24
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 ! Attribute/feature construction ! New attributes constructed from the given ones
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Data Transformation: Normalization
! min-max normalization
! z-score normalization
! normalization by decimal scaling
A A A A A
A
new max new min new min
max min
v min
v ' ( _ − _ )+ _
A
A
stand dev
v mean
v
_
j
v
v
' = Where j is the smallest integer such that Max(| v '|)<
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Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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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 4/7/2003 Data Mining: Concepts and Techniques 28
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 ! Queries regarding aggregated information should be answered using data cube, when possible
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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 ! Heuristic methods (due to exponential # of choices): ! step-wise forward selection ! step-wise backward elimination ! combining forward selection and backward elimination ! decision-tree induction 4/7/2003 Data Mining: Concepts and Techniques 30
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}
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Numerosity Reduction
! Parametric methods ! Assume the data fits some model, estimate model parameters, store only the parameters, and discard the data (except possible outliers) ! Log-linear models: obtain value at a point in m-D space as the product on appropriate marginal subspaces ! Non-parametric methods ! Do not assume models ! Major families: histograms, clustering, sampling
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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
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! Linear regression:Y = α + β X ! Two parameters , α and β specify the line and are to be estimated by using the data at hand. ! using the least squares criterion to the known values ofY 1 , Y 2 , Ö, X 1 , X 2 , Ö. ! 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) = αab βacχad δbcd
Regress Analysis and Log- Linear Models
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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
(^100002000030000400005000060000700008000090000100000)
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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 ìsmearedî
! 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
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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 ! Sampling may not reduce database I/Os (page at a time).
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Sampling
SRSWOR
(simple random sample without replacement)
SRSWR
Raw Data 4/7/2003 Data Mining: Concepts and Techniques 44
Sampling
Raw Data Cluster/Stratified Sample
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Hierarchical Reduction
! Use multi-resolution structure with different degrees of reduction ! Hierarchical clustering is often performed but tends to define partitions of data sets rather than ìclustersî ! Parametric methods are usually not amenable to hierarchical representation ! Hierarchical aggregation ! An index tree hierarchically divides a data set into partitions by value range of some attributes ! Each partition can be considered as a bucket ! Thus an index tree with aggregates stored at each node is a hierarchical histogram 4/7/2003 Data Mining: Concepts and Techniques 46
Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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Discretization
! Three types of attributes: ! Nominal ó values from an unordered set ! Ordinal ó values from an ordered set ! Continuous ó 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
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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).
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Specification of a set of attributes
Concept hierarchy can be automatically generated based on the number of distinct values per attribute in the given attribute set. The attribute with the most distinct values is placed at the lowest level of the hierarchy.
country
province_or_ state
city
street
15 distinct values
65 distinct values
3567 distinct values
674,339 distinct values 4/7/2003 Data Mining: Concepts and Techniques 56
Chapter 3: Data Preprocessing
! Why preprocess the data?
! Data cleaning
! Data integration and transformation
! Data reduction
! Discretization and concept hierarchy generation
! Summary
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Summary
! Data preparation is an important issue for both warehousing and mining
! Data preparation includes
! Data cleaning and data integration
! Data reduction and feature selection
! Discretization
! Many methods have been developed but still an active area of research
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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.
