introduction-to-knowledge discovery in database
TRANSCRIPT
Chapter 1 :
Presented By :-Kartik N. Kalpande.
What is Knowledge Acquisitions ? aka :: data mining, knowledge discovery,
knowledge extraction, information discovery, information harvesting ect.
Process of discovering useful information,hidden pattern or rules in large quantities of data ( non-trivial, unknown data)
By automatic or semiautomatic means It’s impossible to find pattern using manual
method.
Why Knowledge Acquisitions ?
Why Knowledge Acquisitions ? Why?
Data explosion (tremendous amount of data available) Data is being warehoused Computing power Competitive pressure
Hard Disk Nowadays more than 100Ggbytes capacities
Is Data Mining Appropriate for My problem ? Four general question to consider
Can we clearly define the problem? Does potentially meaningful data exist? Does the data contain hidden knowledge or is the
data factual and useful for reporting purpose only? Will the cost of processing the data be less than
the likely increase in profit seen by applying any potential knowledge gained from the data mining project.
Traditional Approaches Traditional database queries:. Access a
database using a well defined query such as SQL
The query output consist of data from database
The output usually a subset of the database
DBMS DB
SQL
Data Mining or Data Query Four general types of knowledge can be
define to help us determine when data mining is appropriate.Shallow KnowledgeMultidimensional KnowledgeHidden KnowledgeDeep Knowledge
Shallow Knowledge Factual in nature Can be easily stored and manipulated in a
database Database query language such as SQL
are excellent tools for extracting shallow knowledge from data
Multidimensional Knowledge also Factual Data are stored in a multidimensional
format On-line Analytical Processing (OLAP)
tools are used on multidimensional data
Hidden Knowledge Patterns or regularities in data that cannot
be easily found using database query language such as SQL
Data mining algorithms can find such patterns with ease.
Deep Knowledge Knowledge stored in database that can
only be found if we are given some direction about what we are looking for.
Current data mining tools are not able to locate deep knowledge.
What can computers learn?• Four level of learning can be differentiated
(Merril & Tennyson, 1977) : Facts : simple statement of truth Concepts : set of objects, symbols, or events grouped
together because they share certain characteristics Procedures: step by step course of action to achieve a
goal. Principles: highest level of learning. General truth or
laws that are basic to other truths.
What can computers learn?• Computer are good at learning ‘concepts’.• Concepts are the output of data mining
session.• There are three (3) common concept view:
a. Classical viewb. Probabilistic viewc. Exemplar View
Three Concept Viewsa. Classical View:• Definite defining properties• These properties determine if an individual item is an
example of a particular concept.• Crisp and leaves no room for misinterpretation.• Example: Good Credit Rating
IF Annual Income >= 30,000& Years at Current Position >= 5& Owns Home = TrueTHEN Good Credit Risk = True
Three Concept Viewsb. Probabilistic View:• Concepts are represented by properties that are probable of concept member.• Assumption is that people store and recall concept as generalization created
from individual instance observation.• Cannot be directly applied to achieve answer – but can be used to help in
decision making process.• Associate probability of membership with a specific
classification.
- The mean annual income for individuals who consistently make loan payments on time is $30,000- Most individuals who are good credit risks have been working for the same company for at least five years.- The majority of good credit risks own their own home
Three Concept Viewsb. Probabilistic View:• Example: Good Credit Rating
Home owner with an annual income of $27000, employed at the same position for 4 years might be classified as a good credit risk with a probability of 0.85
Three Concept Viewsc. Exemplar View:• A given instance is determine to be an example of a particular concept
if the instance is similar enough to a set of one or more known examples of the concept .
• Assumption is that people store and recall likely concept exemplars that are then used to classify new instances.
• Can associate a probability of concept membership with each classification.
Three Concept Viewsc. Exemplar View:• Example:
Exemplar #1: Annual Income = 32,000 Number of years at current position = 6 Homeowner
Exemplar #2: Annual Income = 52,000 Number of years at current position = 16 Renter
Exemplar #1: Annual Income = 28,000 Number of years at current position = 12 Homeowner
What can be mined?
Concepts that can be mined?
a. Classes :• stored data is used to locate data in
predetermined groups.• Eg: A restaurant chain could mine
customer purchase data to determine when customers visit and what they typically order.
Concepts that can be mined?
b. Clusters :• Data items are grouped by logical
relationships.• Eg: Data can be mined to identify market
segments or customer affinities.
Concepts that can be mined?
c. Associations :• Data can be mined to identify
association.• Eg: The beer-diaper example is typical of
associative mining.
Concepts that can be mined?
d. Sequential :• Patterns in which data is mined to
anticipate behavior patterns and trends.• Eg: An outdoor equipment retailer could
predict the likelihood of a backpack purchase based on sleeping bag or hiking shoes sale.
Multidisciplinary
Databases
StatisticsPatternRecognition
KDD
MachineLearning AI
Neurocomputing
Data Mining
Disciplines Of Data Mining
Data Mining
Information RetrivalAlgorithm
Machine Learning Visualization
StatisticsDatabase System
Data Mining Model & Task
Data Mining
Predictive Descriptive
•Classification•Regression•Time Series Analysis•Prediction
•Clustering•Summarization•Association Rules•Sequence Discovery
Predictive Model Make prediction about values of data using
known results found from different data Or based on the use of other historical data Example:: credit card fraud, breast cancer
early warning, terrorist act, tsunami and ect.
Predictive Model Perform inference on the current data to make
predictions. We know what to predict based on historical data) Never accurate 100% Concentrate more to input output relation ship
( x,f(x)) Typical Question
Which costumer are likely to buy this product next four month
What kind of transactions that are likely to be fraudulent
Who is likely to drop this paper?
Predictive Model
xx xxx
xx
xx
xx
x xxx
x
months
Profit (RM)
Current data
Future dataO ?
Descriptive Model Identifies pattern or relationships in data. Serves as a way to explore the properties of
data examined, not to predict new properties Always required a domain expert Example::
Segmenting marketing area Profiling student performances
Descriptive Model Discovering new patterns inside the data We may don’t have any idea how the data looks like Explores the properties of the data examined Pattern at various granularities (eg: Student:
University-> faculty->program-> major? Typical Question
What is the data What does it look like What does the data suggest for group of customer
advertisement?
Descriptive Model
major
Results
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Group 1Group 2
Group 3
View Of DM Data To Be Mined
Data warehouse, WWW, time series, textual. spatial multimedia, transactional
Knowledge To Be Mined Classification, prediction, summarization, trend
Techniques Utilized Database, machine learning, visualization, statistics
Applications Adapted Marketing, demographic segmentation, stock
analysis
DM In Action Medical Applications ::clinical diagnosis, drug analysis Business (marketing segmentation & strategies,
insolvency predictor, loan risk assessment Education (Online learning) Internet (searching engine) Etc.
Data Mining Methodology Hypothesis Testing vs Knowledge Discovery
Hypothesis Testing Top down approach Attempts to substantiate or disprove preconceived idea
Knowledge Discovery Bottom-up approach Start with data and tries to get it to tell us something we
didn’t already know
Data Mining Methodology Hypothesis Testing
Generate good ideas Determine what data allow these hypotheses
to be tested Locate the data Prepare the data for analysis Build computer models based on the data Evaluate computer model to confirm or reject
hypotheses
Data Mining Methodology Knowledge Discovery
Directed Identified sources of pre classified data Prepare data analysis Select appropriated KD techniques based on data
characteristics and data mining goal Divide data into training, testing and evaluation Use the training dataset to build model Tune the model by applying it to test dataset Take action based on data mining results Measure the effect of the action taken Restart the DM process taking advantage of new data
generated by the action taken
Data Mining Methodology Knowledge Discovery
Undirected Identified available data sources Prepare data analysis Select appropriated undirected KD techniques based
on data characteristics and data mining goal Use the selected technique to uncover hidden
structure in the data Identify potential targets for directed KD Generate new hypothesis to test
Question for Group Discussion
Revision::Two Approaches In data Mining
Data Mining
Predictive Descriptive
•Classification•Regression•Time Series Analysis•Prediction
•Clustering•Summarization•Association Rules•Sequence Discovery
Predict the future value Define R/S among data
Knowledge Discovery Process
Knowledge Discovery Process 1.0 Selection
The data needs for the data mining process may be obtained from many different and heterogeneous data sources
Examples Business Transactions Scientific Data Video and pictures
Knowledge Discovery Process 2.0 Pre Processing Main idea – to ensure that data is clean (high quality of
data). The data to be used by the process may have
incorrect or missing data. There may be anomalous data from multiple
sources involving different data types and metrics
Erroneous data may be corrected or removed, whereas missing data must be supplied or predicted (Often using data mining tools)
Knowledge Discovery Process 3.0 Transformation
Data from different sources must be converted into a common format for processing
Some data may be encoded or transformed into more usable formats
Example:: Data Reduction Data Cleaning, Data Integration,
Data Transformation, Data Reduction and Data Discretization
Knowledge Discovery Process 4.0 Data Mining Main idea –to use intelligent method to extract
patterns and knowledge from database This step applies algorithms to the transformed
data to generate the desired results. The heart of KD process (where unknown pattern will
be revealed). Example of algorithms: Regression
(classification, prediction), Neural Networks (prediction, classification, clustering), Apriori Algorithms (association rules), K-Means & K-Nearest Neighbor (clustering), Decision Tree (classification), Instance Learning (classification).
Knowledge Discovery Process 5.0 Interpretation/Evaluation
How the data mining results are presented to the users is extremely important because the usefulness of the results is dependent on it
Example:: Graphical Geometric Icon Based Pixel Based Hierarchical Based Hybrid
Case Study: Predicting FSK Final Year’s Student Performance
activities
Student database {contains 30,000 records}
Academicsacademics
Selected record {matric, PMK, grades} – only 2,000 records (contains incomplete records etc.
Selectionacademics
Clean record {replace the missing value, removed the replicated}
Pre-processing Using neural networks : transform into numerical.
Transformation
Y=w1x1+w2x2+b1
Generated Model : pattern for performance prediction
Data mining
Testing result: 90 % correct
accept model
Knowledge (apply model)
Interpretation & evaluation