Data Mining Lecture 2: DBMS, DW, OLAP, and Data Preprocessing.
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Transcript of Data Mining Lecture 2: DBMS, DW, OLAP, and Data Preprocessing.
Data Mining
Lecture 2: DBMS, DW, OLAP, and Data Preprocessing
Contrasting Database and File Systems
An Example of a Simple Relational Database
The Relational Schema for the SaleCo Database
The Entity Relationship Model
The Development of Data Models
The Relational Schema for the TinyCollege Database
The Database System Environment
Data Warehouse
Data Life Cycle Process Continued
The result - generating knowledgeThe result - generating knowledge
Methods for Collecting Raw Data
• Collection can take place– in the field
– from individuals
– via manually methods• time studies• Surveys• Observations• contributions from experts
– using instruments and sensors
– Transaction processing systems (TPS)
– via electronic transfer
– from a web site (Clickstream)
The task of data collection is fairly complex. Which can create data-quality problem requiring validation and cleansing of data.
The Need for Data Analysis
• Managers must be able to track daily transactions to evaluate how the business is performing
• By tapping into the operational database, management can develop strategies to meet organizational goals
• Data analysis can provide information about short-term tactical evaluations and strategies
Transforming Operational Data Into Decision Support Data
The Data Warehouse
• Benefits of a data warehouse are:– The ability to reach data quickly, since they are located in one place
– The ability to reach data easily and frequently by end users with Web browsers.
A data warehouse is a repository of subject-oriented historical data that is organized to be accessible in a form readily acceptable for analytical processing activities (such as data mining, decision support, querying, and other applications).
The Data Warehouse Continued
• Characteristics of data warehousing are:– Time variant. The data are kept for many years so they can
be used for trends, forecasting, and comparisons over time.
– Nonvolatile. Once entered into the warehouse, data are not updated.
– Relational. Typically the data warehouse uses a relational structure.
– Client/server. The data warehouse uses the client/server architecture mainly to provide the end user an easy access to its data.
– Web-based. Data warehouses are designed to provide an efficient computing environment for Web-based applications
The Data Warehouse Continued
Conceptual Modeling of Data Warehouses
• Modeling data warehouses: dimensions & measures– Star schema: A fact table in the middle connected to a set of
dimension tables
– Snowflake schema: A refinement of star schema where some
dimensional hierarchy is normalized into a set of smaller
dimension tables, forming a shape similar to snowflake
– Fact constellations: Multiple fact tables share dimension tables,
viewed as a collection of stars, therefore called galaxy schema or
fact constellation
Example of Star Schema
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Example of Snowflake Schema
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcity_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_key
item
branch_keybranch_namebranch_type
branch
supplier_keysupplier_type
supplier
city_keycityprovince_or_streetcountry
city
Example of Fact Constellation
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_keyshipper_namelocation_keyshipper_type
shipper
The Data Cube
• One intersection might be the quantities of a product sold by specific retail locations during certain time periods.
• Another matrix might be Sales volume by department, by day, by month, by year for a specific region
• Cubes provide faster:– Queries– Slices and Dices of the information– Rollups– Drill Downs
Multidimensional databases (sometimes called OLAP) are specialized data stores that organize facts by dimensions, such as geographical region, product line, salesperson, time. The data in these databases are usually preprocessed and stored in data cubes.
Three-Dimensional View of Sales
Cube: A Lattice of Cuboids
all
time item location supplier
time,item time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,location
time,item,supplier
time,location,supplier
item,location,supplier
time, item, location, supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
Operational vs. Multidimensional View of Sales
Creating a Data Warehouse
OLTP and OLAP
Transactional vs. Analytical Data Processing
Transactional processing takes place in operational systems (TPS) that provide the organization with the capability to perform business transactions and produce transaction reports. The data are organized mainly in a hierarchical structure and are centrally processed. This is done primarily for fast and efficient processing of routine, repetitive data.
A supplementary activity to transaction processing is called analytical processing, which involves the analysis of accumulated data. Analytical processing, sometimes referred to as business intelligence, includes data mining, decision support systems (DSS), querying, and other analysis activities. These analyses place strategic information in the hands of decision makers to enhance productivity and make better decisions, leading to greater competitive advantage.
OLTP vs. OLAP OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date detailed, flat relational isolated
historical, summarized, multidimensional integrated, consolidated
usage repetitive ad-hoc
access read/write index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
OLAP Client/Server Architecture
OLAP Server Arrangement
OLAP Server with Multidimensional Data Store Arrangement
OLAP Server With Local Mini Data Marts
Data Mining: Extraction of Knowledge From Data
Review: Data-Mining Phases
Data Preprocessing
Data Preprocessing
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
Why Data Preprocessing?
• Data in the real world is a mess– 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
Cont’d
• Just as manufacturing and refining are about transformation of raw materials into finished products, so too with data to be used for data mining
• ECTL – extraction, clean, transform, load – is the process/methodology for preparing data for data mining
• The goal: ideal DM environment
Data Types
• Variable Measures– Categorical variables (e.g., CA, AZ, UT…)– Ordered variables (e.g., course grades)– Interval variables (e.g., temperatures)– True numeric variables (e.g., money)
• Dates & Times• Fixed-Length Character Strings (e.g., Zip Codes)• IDs and Keys – used for linkage to other data in other
tables• Names (e.g., Company Names)• Addresses• Free Text (e.g., annotations, comments, memos, email)• Binary Data (e.g., audio, images)
Multi-Dimensional Measure of Data Quality
• A well-accepted multidimensional view:– Accuracy– Completeness– Consistency– Timeliness– Believability– Value added– Interpretability– Accessibility
• Broad categories:– intrinsic, contextual, representational, and
accessibility.
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
Forms of data preprocessing
What the Data Should Look Like
• All data mining algorithms want their input in tabular form – rows & columns as in a spreadsheet or database table
i.e. Give a sample file of SPSS
What the Data Should Look Like
• Customer Signature– Continuous “snapshot” of customer behavior
Each row representsthe customer and whatever might be useful for data mining
What the Data Should Look Like
• The columns– Contain data that describe aspects of the
customer (e.g., sales $ and quantity for each of product A, B, C)
– Contain the results of calculations referred to as derived variables (e.g., total sales $)
What the Data Should Look Like
1. Columns with One Value - Often not very useful
2. Columns with Almost Only One Value
3. Columns with Unique Values
4. Columns Correlated with Target Variable (synonyms with the target variable)
1. 2. 3.
Data Cleaning
• Data cleaning tasks
– Fill in missing values
– Identify outliers and smooth out noisy data
– Correct inconsistent data
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.
How to Handle Missing Data?
• Ignore the tuple: usually done when class label is missing (assuming the
tasks in classification—not 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., “unknown”, 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
• Use the most probable value to fill in the missing value: inference-based
such as Bayesian formula or decision tree
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
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
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.
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
Cluster Analysis
Regression
x
y
y = x + 1
X1
Y1
Y1’
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
Handling Redundant Data in Data Integration
• Redundant data occur often when integration of multiple databases– The same attribute may have different names in different
databases
– One attribute may be a “derived” 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
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
Data Transformation: Normalization
• min-max normalization
• z-score normalization
• normalization by decimal scaling
AAA
AA
A
minnewminnewmaxnewminmax
minvv _)__('
A
A
devstand
meanvv
_'
j
vv
10' Where j is the smallest integer such that Max(| |)<1'v
• Given N data vectors from k-dimensions, find c <= k orthogonal vectors that can be best used to represent data – The original data set is reduced to one consisting of N
data vectors on c principal components (reduced dimensions)
• Each data vector is a linear combination of the c principal component vectors
• Works for numeric data only
• Used when the number of dimensions is large
Principal Component Analysis
X1
X2
Y1
Y2
Principal Component Analysis
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
• Linear regression: Y = + X– Two parameters , and 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, …, X1, X2, ….
• 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) = ab acad bcd
Regress Analysis and Log-Linear Models
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).
Sampling
SRSWOR
(simple random
sample without
replacement)
SRSWR
Raw Data
Sampling
Raw Data Cluster/Stratified Sample
References• Design and Implementation of Database Systems (2005), Rob
• Michael J. A. Berry and Gordon S. Linoff (2004), Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd ed., Wiley
• Introduction to Data Mining and Knowledge Discovery, Third Edition, ISBN: 1-892095-02-5 (Can be downloaded via website for free)
• Tan, P., Steinbach, M., and Kumar, V. (2006) Introduction to Data Mining, 1st edition, Addison-Wesley, ISBN: 0-321-32136-7.
• Vasant Dhar and Roger Stein, Prentice-Hall (1997), Seven Methods for Transforming Corporate Data Into Business Intelligence
• H. Witten and E. Frank (2005), Data Mining:Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufmann, ISBN: 0-12-088407-0, closely tied to the WEKA software.
• Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, October 2004, ISBN 0-262-01211-1
• J. Han and M. Kamber (2000) Data Mining: Concepts and Techniques, Morgan Kaufmann. Database oriente.