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

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

supplier_keysupplier_type

supplier

city_keycityprovince_or_streetcountry

city

Example of Fact Constellation


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

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


• Customer Signature– Continuous “snapshot” of customer behavior

Each row representsthe customer and whatever might be useful for data mining


• 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 $)


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.

Data Mining Lecture 2: DBMS, DW, OLAP, and Data Preprocessing

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